Исследование объявлений о продаже квартир¶

Цель исследования:
Основная цель проекта заключается в проведении комплексного анализа данных о рынке недвижимости Санкт-Петербурга и разработке методологии определения рыночной стоимости квартир на основе множества параметров.

По каждой квартире на продажу доступны два вида данных. Первые вписаны пользователем, вторые — получены автоматически на основе картографических данных. Например, расстояние до центра, аэропорта и других объектов — эти данные автоматически получены из геосервисов. Количество парков и водоёмов также заполняется без участия пользователя.

Описание данных:

  • airports_nearest — расстояние до ближайшего аэропорта в метрах (м)
  • balcony — число балконов
  • ceiling_height — высота потолков (м)
  • cityCenters_nearest — расстояние до центра города (м)
  • days_exposition — сколько дней было размещено объявление (от публикации до снятия)
  • first_day_exposition — дата публикации
  • floor — этаж
  • floors_total — всего этажей в доме
  • is_apartment — апартаменты (булев тип)
  • kitchen_area — площадь кухни в квадратных метрах (м²)
  • last_price — цена на момент снятия с публикации
  • living_area — жилая площадь в квадратных метрах (м²)
  • locality_name — название населённого пункта
  • open_plan — свободная планировка (булев тип)
  • parks_around3000 — число парков в радиусе 3 км
  • parks_nearest — расстояние до ближайшего парка (м)
  • ponds_around3000 — число водоёмов в радиусе 3 км
  • ponds_nearest — расстояние до ближайшего водоёма (м)
  • rooms — число комнат
  • studio — квартира-студия (булев тип)
  • total_area — общая площадь квартиры в квадратных метрах (м²)
  • total_images — число фотографий квартиры в объявлении

Загрузка данных¶

In [1]:
# Импорт необходимых библиотек
import pandas as pd
import matplotlib.pyplot as plt
from google.colab import drive
In [2]:
# Настройка отоброжения и подключение Google Colab
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [3]:
try:
    data = pd.read_csv('/datasets/real_estate_data.csv', sep='\t')  # Для практикума
except FileNotFoundError:
    try:
        data = pd.read_csv('Модуль 1 спринт 1/real_estate_data.csv', sep='\t')  # Если файл локальный
    except FileNotFoundError:
        try:
            data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Модуль 1/Модуль 1 спринт 1/real_estate_data.csv', sep='\t')  # Для Google Colab
        except FileNotFoundError:
            print('Файл не найден ни в одном из указанных мест.')
            contract = None

Выполним первичный анализ данных.

In [4]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23699 entries, 0 to 23698
Data columns (total 22 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   total_images          23699 non-null  int64  
 1   last_price            23699 non-null  float64
 2   total_area            23699 non-null  float64
 3   first_day_exposition  23699 non-null  object 
 4   rooms                 23699 non-null  int64  
 5   ceiling_height        14504 non-null  float64
 6   floors_total          23613 non-null  float64
 7   living_area           21796 non-null  float64
 8   floor                 23699 non-null  int64  
 9   is_apartment          2775 non-null   object 
 10  studio                23699 non-null  bool   
 11  open_plan             23699 non-null  bool   
 12  kitchen_area          21421 non-null  float64
 13  balcony               12180 non-null  float64
 14  locality_name         23650 non-null  object 
 15  airports_nearest      18157 non-null  float64
 16  cityCenters_nearest   18180 non-null  float64
 17  parks_around3000      18181 non-null  float64
 18  parks_nearest         8079 non-null   float64
 19  ponds_around3000      18181 non-null  float64
 20  ponds_nearest         9110 non-null   float64
 21  days_exposition       20518 non-null  float64
dtypes: bool(2), float64(14), int64(3), object(3)
memory usage: 3.7+ MB
In [5]:
data.head(10)
Out[5]:
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest cityCenters_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
0 20 13000000.0 108.00 2019-03-07T00:00:00 3 2.70 16.0 51.00 8 NaN False False 25.00 NaN Санкт-Петербург 18863.0 16028.0 1.0 482.0 2.0 755.0 NaN
1 7 3350000.0 40.40 2018-12-04T00:00:00 1 NaN 11.0 18.60 1 NaN False False 11.00 2.0 посёлок Шушары 12817.0 18603.0 0.0 NaN 0.0 NaN 81.0
2 10 5196000.0 56.00 2015-08-20T00:00:00 2 NaN 5.0 34.30 4 NaN False False 8.30 0.0 Санкт-Петербург 21741.0 13933.0 1.0 90.0 2.0 574.0 558.0
3 0 64900000.0 159.00 2015-07-24T00:00:00 3 NaN 14.0 NaN 9 NaN False False NaN 0.0 Санкт-Петербург 28098.0 6800.0 2.0 84.0 3.0 234.0 424.0
4 2 10000000.0 100.00 2018-06-19T00:00:00 2 3.03 14.0 32.00 13 NaN False False 41.00 NaN Санкт-Петербург 31856.0 8098.0 2.0 112.0 1.0 48.0 121.0
5 10 2890000.0 30.40 2018-09-10T00:00:00 1 NaN 12.0 14.40 5 NaN False False 9.10 NaN городской посёлок Янино-1 NaN NaN NaN NaN NaN NaN 55.0
6 6 3700000.0 37.30 2017-11-02T00:00:00 1 NaN 26.0 10.60 6 NaN False False 14.40 1.0 посёлок Парголово 52996.0 19143.0 0.0 NaN 0.0 NaN 155.0
7 5 7915000.0 71.60 2019-04-18T00:00:00 2 NaN 24.0 NaN 22 NaN False False 18.90 2.0 Санкт-Петербург 23982.0 11634.0 0.0 NaN 0.0 NaN NaN
8 20 2900000.0 33.16 2018-05-23T00:00:00 1 NaN 27.0 15.43 26 NaN False False 8.81 NaN посёлок Мурино NaN NaN NaN NaN NaN NaN 189.0
9 18 5400000.0 61.00 2017-02-26T00:00:00 3 2.50 9.0 43.60 7 NaN False False 6.50 2.0 Санкт-Петербург 50898.0 15008.0 0.0 NaN 0.0 NaN 289.0
In [6]:
data.tail()
Out[6]:
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest cityCenters_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
23694 9 9700000.0 133.81 2017-03-21T00:00:00 3 3.7 5.0 73.3 3 NaN False False 13.83 NaN Санкт-Петербург 24665.0 4232.0 1.0 796.0 3.0 381.0 NaN
23695 14 3100000.0 59.00 2018-01-15T00:00:00 3 NaN 5.0 38.0 4 NaN False False 8.50 NaN Тосно NaN NaN NaN NaN NaN NaN 45.0
23696 18 2500000.0 56.70 2018-02-11T00:00:00 2 NaN 3.0 29.7 1 NaN False False NaN NaN село Рождествено NaN NaN NaN NaN NaN NaN NaN
23697 13 11475000.0 76.75 2017-03-28T00:00:00 2 3.0 17.0 NaN 12 NaN False False 23.30 2.0 Санкт-Петербург 39140.0 10364.0 2.0 173.0 3.0 196.0 602.0
23698 4 1350000.0 32.30 2017-07-21T00:00:00 1 2.5 5.0 12.3 1 NaN False False 9.00 NaN поселок Новый Учхоз NaN NaN NaN NaN NaN NaN NaN

Гистограммы по числовым столбцам датафрейма

In [7]:
fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(15, 20))
axes = axes.flatten()

for i, j in enumerate(data.select_dtypes(include='number')):
    axes[i].hist(data[j], bins=50, color='skyblue')
    axes[i].grid(True)
    axes[i].set_title(f'{j}')
    axes[i].set_xlabel(j)
    axes[i].set_ylabel('Количество')

# Убираем пустые подграфики, если они есть
for j in range(i + 1, len(axes)):
    fig.delaxes(axes[j])

plt.tight_layout()  # Корректируем расположение графиков
plt.show()
No description has been provided for this image

Промежуточный вывод:

  • Графики (слева на право) 2, 3, 4, 5, 7, 9, 14, 17 имеют не хакетерный для гистограмм, тип распределния
  • Столбец its_apertment должене иметь булевый тип данных
  • Столбецы first_day_exposition и нужно преобразовать в datetime
  • Требуется выоплнить предобработку данных из за наличии пропусков

Предобработка данных¶

In [8]:
# Определим количество пропущенных значений в каждом столбце
data.isna().sum()
Out[8]:
0
total_images 0
last_price 0
total_area 0
first_day_exposition 0
rooms 0
ceiling_height 9195
floors_total 86
living_area 1903
floor 0
is_apartment 20924
studio 0
open_plan 0
kitchen_area 2278
balcony 11519
locality_name 49
airports_nearest 5542
cityCenters_nearest 5519
parks_around3000 5518
parks_nearest 15620
ponds_around3000 5518
ponds_nearest 14589
days_exposition 3181

Так как наша задача состоит в том, чтобы определить рыночную стоимость объектов недвижимости, то постараемся вычленить те столбцы которые нужно обязательно спасти от пропусков.

  • ceiling_height - высота потолков, должна иметь влияние на итоговую стоимость недвижимости
  • floors_tota - малое количество пропусков в столбце количества этажей, что меньше 0,36% от количества всех объявлений
  • living_area - жилая площадь квартиры, нужно сохранить как можно больше данных
  • is_apartment - более 88% пропусков в столбце
  • kitchen_area - нужно сохранить больше данных
  • balcony - заменим NaN на 0?
  • locality_name - название населённого пункта, видимо данны останутся утерянными, подумаем...

Данные полученные автоматически на основе картографических данных, также имеют пропуски. В теории пропуски можно заменить категориальными данными, взяв в основу гипотезу, чем дальше от центра тем квартира дороже.

In [9]:
# Переименуем столбец cityCenters_nearest
data = data.rename(columns={'cityCenters_nearest':'city_centers_nearest'})

Рассмотрим подробнее каждый столбец, начнем с total_images

In [10]:
# Построим гистограмму для столбца total_images
data['total_images'].hist(bins=50, figsize=(10, 5));
No description has been provided for this image
In [11]:
# Посчитаем количество строк где фотографий от 20
len(data[(20 < data['total_images']) & (data['total_images'] < 50)])
Out[11]:
72

Основном объявления недвижимости имеют количество фотографий от 10 до 20 фотографий. Данные выглядят правдоподобно. Если 72 случая - аномальные значения, считаю это допустимой погрешностью

Рассмотрим подробнее столбец last_price

In [12]:
data['last_price'].hist(bins=100, figsize=(10, 5));
No description has been provided for this image
In [13]:
data['last_price'].describe() / 1000000
Out[13]:
last_price
count 0.023699
mean 6.541549
std 10.887013
min 0.012190
25% 3.400000
50% 4.650000
75% 6.800000
max 763.000000

In [14]:
data['last_price'].hist(bins=100, range=(0, 15000000), figsize=(10, 5))
plt.title('Цена продажи')
Out[14]:
Text(0.5, 1.0, 'Цена продажи')
No description has been provided for this image
In [15]:
data['last_price'].hist(bins=100, range=(15000000, 30000000), figsize=(10, 5))
plt.title('Цена продажи')
Out[15]:
Text(0.5, 1.0, 'Цена продажи')
No description has been provided for this image
In [16]:
data['last_price'].hist(bins=1000, range=(30000000, 1000000000), figsize=(10, 5))
plt.title('Цена продажи')
Out[16]:
Text(0.5, 1.0, 'Цена продажи')
No description has been provided for this image

Самая дешевая квартира стоит 12190 рублей, а самая дорогая 763000000 рублей. Данные выглядят правдоподобно!

Изучим столбец total_area

In [17]:
data['total_area'].hist(bins=100, figsize=(15, 5))
plt.title('Общая площадь')
Out[17]:
Text(0.5, 1.0, 'Общая площадь')
No description has been provided for this image
In [18]:
data['total_area'].describe()
Out[18]:
total_area
count 23699.000000
mean 60.348651
std 35.654083
min 12.000000
25% 40.000000
50% 52.000000
75% 69.900000
max 900.000000

In [19]:
data['total_area'].hist(bins=100, range=(200, 900), figsize=(15, 5))
plt.title('Общая площадь');
No description has been provided for this image
In [20]:
# посчитаем количество строк с площадью от 200 метров в квадрате

len(data[(200 <= data['total_area']) & (data['total_area'] <= 1000)])
Out[20]:
231

В целом данные выглядят правдоподобно, 231 считаю приемлемой погрешностью

Преобразуем столбец first_day_exposition в datetime

In [21]:
# Используем метод to_datetime

data['first_day_exposition'] = pd.to_datetime(data['first_day_exposition'], format='%Y-%m-%dT%H:%M:%S')
In [22]:
data['first_day_exposition'].head()
Out[22]:
first_day_exposition
0 2019-03-07
1 2018-12-04
2 2015-08-20
3 2015-07-24
4 2018-06-19

Столбец принял формат datetime!

Рассмотрим столбец rooms

In [23]:
data.plot(y='rooms', kind='hist', figsize=(10, 5), grid=True, title='Количество комнат');
No description has been provided for this image
In [24]:
data['rooms'].describe()
Out[24]:
rooms
count 23699.000000
mean 2.070636
std 1.078405
min 0.000000
25% 1.000000
50% 2.000000
75% 3.000000
max 19.000000

Изучим сколько квартир с 0 комнат, возможно это студии!

In [25]:
# Проверим количество строк, где количество комнат 0

data.query('rooms == 0').shape[0]
Out[25]:
197

Всего таких столбцов 197. Изучим сколько таких столбцов, где количество комнат равно нулю и студия равно True

In [26]:
data.query('rooms == 0 and studio == True').shape[0]
Out[26]:
138

Таких строк 138, можно прийти к итогу, что 138 строк это студии. А остальную часть, раcсмотрим подробнее

In [27]:
display(data.query('rooms == 0 and studio == False').head())
data.query('rooms == 0 and studio == False').tail()
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest city_centers_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
349 4 2320000.0 25.0 2017-09-27 0 NaN 14.0 17.0 1 NaN False True NaN 0.0 деревня Кудрово NaN NaN NaN NaN NaN NaN 45.0
508 0 3375000.0 34.4 2017-03-28 0 NaN 26.0 24.3 19 NaN False True NaN 1.0 посёлок Парголово 53115.0 19292.0 0.0 NaN 1.0 593.0 430.0
780 9 2600000.0 26.1 2018-02-20 0 NaN 18.0 NaN 8 NaN False True NaN 1.0 посёлок Мурино NaN NaN NaN NaN NaN NaN 45.0
839 14 1900000.0 35.0 2017-04-14 0 2.7 5.0 15.0 3 False False True NaN 1.0 поселок Новый Свет NaN NaN NaN NaN NaN NaN 7.0
946 5 2200000.0 23.0 2016-09-27 0 NaN 27.0 18.0 7 NaN False True NaN 2.0 посёлок Мурино NaN NaN NaN NaN NaN NaN 483.0
Out[27]:
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest city_centers_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
21299 13 2450000.0 28.5 2017-11-22 0 NaN 12.0 18.0 7 NaN False True NaN 2.0 Санкт-Петербург 20381.0 19383.0 0.0 NaN 1.0 579.0 73.0
21628 9 3900000.0 34.0 2017-03-18 0 NaN 20.0 24.0 10 NaN False True NaN 2.0 Санкт-Петербург 15506.0 13615.0 0.0 NaN 1.0 767.0 320.0
22573 8 2250000.0 24.0 2017-09-28 0 NaN 12.0 15.0 3 NaN False True NaN 2.0 городской посёлок Новоселье NaN NaN NaN NaN NaN NaN 45.0
23442 0 2500000.0 27.7 2017-09-28 0 NaN 24.0 18.0 4 NaN False True NaN 2.0 посёлок Мурино NaN NaN NaN NaN NaN NaN 4.0
23592 10 1450000.0 29.0 2017-11-11 0 NaN 9.0 18.0 8 NaN False True NaN 2.0 Никольское NaN NaN NaN NaN NaN NaN 82.0

По выведенным строки можно сказать, что часть строк также могу являться студиями. Так как общая площадь квартир зачастую не превышает 30 кв. метров.

In [28]:
# Изучим строки с количеством комнат подробнее, отсортировав их по площади.

data.query('rooms == 0 and studio == False')['total_area'].describe()
Out[28]:
total_area
count 59.000000
mean 32.707288
std 44.986261
min 16.000000
25% 25.000000
50% 26.100000
75% 28.250000
max 371.000000

Часть квартир с площадью меньше 30 переведем в квартиры - судии.

In [29]:
data.loc[(data['rooms'] == 0) & (data['studio'] == False) & (data['total_area'] <= 30), 'studio'] = True
In [30]:
# Проведем проверку замены
data.query('rooms == 0 and studio == True').shape[0]
Out[30]:
187

Изучим строки, где площадь квартиры больше, 30 кв. метров.

In [31]:
data.query('rooms == 0 and studio == False and total_area >= 30')
Out[31]:
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest city_centers_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
508 0 3375000.0 34.40 2017-03-28 0 NaN 26.0 24.3 19 NaN False True NaN 1.0 посёлок Парголово 53115.0 19292.0 0.0 NaN 1.0 593.0 430.0
839 14 1900000.0 35.00 2017-04-14 0 2.70 5.0 15.0 3 False False True NaN 1.0 поселок Новый Свет NaN NaN NaN NaN NaN NaN 7.0
4683 2 3650000.0 35.00 2018-02-01 0 NaN 25.0 23.4 25 NaN False True NaN NaN Санкт-Петербург 43431.0 12188.0 0.0 NaN 0.0 NaN 100.0
6805 7 1850000.0 31.00 2017-11-10 0 NaN 18.0 18.0 10 NaN False True NaN 1.0 Никольское NaN NaN NaN NaN NaN NaN 7.0
7008 4 5200000.0 32.30 2017-09-14 0 NaN 11.0 25.5 4 NaN False True NaN NaN Санкт-Петербург 30824.0 6469.0 1.0 756.0 1.0 936.0 86.0
7237 8 2999000.0 42.63 2017-11-27 0 NaN 16.0 25.7 2 NaN False True NaN 2.0 деревня Кудрово NaN NaN NaN NaN NaN NaN 45.0
17805 17 2600000.0 31.10 2017-10-25 0 NaN 16.0 21.4 14 NaN False True NaN 2.0 Санкт-Петербург 19212.0 22637.0 0.0 NaN 1.0 457.0 44.0
19392 5 71000000.0 371.00 2018-07-26 0 3.57 7.0 NaN 6 NaN False True NaN NaN Санкт-Петербург 25257.0 6060.0 1.0 761.0 1.0 584.0 90.0
19735 1 2800000.0 30.50 2017-10-10 0 2.75 25.0 20.0 2 NaN False True NaN 2.0 деревня Кудрово NaN NaN NaN NaN NaN NaN 13.0
21628 9 3900000.0 34.00 2017-03-18 0 NaN 20.0 24.0 10 NaN False True NaN 2.0 Санкт-Петербург 15506.0 13615.0 0.0 NaN 1.0 767.0 320.0

9 столбцов из 10 определим в 1 комнатные квартиры. Квартиру с площадью 371 кв. метров пока оставим в покое.

In [32]:
data.loc[(data['rooms'] == 0) & (data['studio'] == False) & (data['total_area'] >= 30) & (data['total_area'] != 371), 'rooms'] = 1
In [33]:
data.query('rooms == 0 and studio == False and total_area >= 30')
Out[33]:
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest city_centers_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
19392 5 71000000.0 371.0 2018-07-26 0 3.57 7.0 NaN 6 NaN False True NaN NaN Санкт-Петербург 25257.0 6060.0 1.0 761.0 1.0 584.0 90.0

Обработаем ceiling_height

Столбец - высота потолков имеет пропуски, количество пропусков - 9195

In [34]:
data['ceiling_height'].describe()
Out[34]:
ceiling_height
count 14504.000000
mean 2.771499
std 1.261056
min 1.000000
25% 2.520000
50% 2.650000
75% 2.800000
max 100.000000

In [35]:
data.plot(y='ceiling_height', kind='hist', bins=50, figsize=(10, 5), grid=True, title='Высота потолков');
No description has been provided for this image

Встречаются жилые помещения с высотой потолков 1 метр и 100 метров. Средня высота потолков 2.73 метров, что подходит под критерии ГОСТа, о высоте потолков. Медианное значение чуть ниже, равна 2.70 метров.

In [36]:
# Заменим пропуски медианным значением высоты потолков
median_ceiling_height = data['ceiling_height'].median()
data.loc[data['ceiling_height'].isna(), 'ceiling_height'] = median_ceiling_height
In [37]:
data.plot(y='ceiling_height', kind='hist', bins=50, range=(0, 10), figsize=(10, 5), grid=True, title='Высота потолков');
No description has been provided for this image

Замена прошла успешно

Изучим floors_total

Столбец, всего этажей имеет 86 пропусков, считаю это число незначительным.

In [38]:
data['floors_total'].describe()
Out[38]:
floors_total
count 23613.000000
mean 10.673824
std 6.597173
min 1.000000
25% 5.000000
50% 9.000000
75% 16.000000
max 60.000000

In [39]:
# Ознакомимся с пропусками
data[data['floors_total'].isna()].head(10)
Out[39]:
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest city_centers_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
186 12 11640000.0 65.20 2018-10-02 2 2.65 NaN 30.80 4 NaN False False 12.00 NaN Санкт-Петербург 39197.0 12373.0 1.0 123.0 0.0 NaN 49.0
237 4 2438033.0 28.10 2016-11-23 1 2.65 NaN 20.75 1 NaN False False NaN 0.0 Санкт-Петербург 22041.0 17369.0 0.0 NaN 1.0 374.0 251.0
457 4 9788348.0 70.80 2015-08-01 2 2.65 NaN 38.40 12 NaN False False 10.63 0.0 Санкт-Петербург 37364.0 8322.0 2.0 309.0 2.0 706.0 727.0
671 4 6051191.0 93.60 2017-04-06 3 2.65 NaN 47.10 8 NaN False False 16.80 0.0 Санкт-Петербург 22041.0 17369.0 0.0 NaN 1.0 374.0 123.0
1757 5 3600000.0 39.00 2017-04-22 1 2.65 NaN NaN 9 NaN False False NaN 0.0 Санкт-Петербург 22735.0 11618.0 1.0 835.0 1.0 652.0 77.0
1930 1 9750000.0 66.77 2016-05-13 2 2.65 NaN NaN 4 NaN False False NaN 0.0 Санкт-Петербург 39886.0 10660.0 1.0 2276.0 3.0 244.0 90.0
2392 7 5395770.0 53.00 2017-06-29 2 2.65 NaN 29.00 22 NaN False False 9.00 0.0 Санкт-Петербург 43074.0 10820.0 0.0 NaN 0.0 NaN 48.0
2846 4 2325000.0 27.80 2016-09-21 1 2.65 NaN 19.40 15 NaN False False NaN 0.0 Санкт-Петербург 22041.0 17369.0 0.0 NaN 1.0 374.0 307.0
2952 10 6500000.0 54.30 2019-04-23 1 2.65 NaN 22.20 6 NaN False False 12.30 NaN Санкт-Петербург 23606.0 3148.0 0.0 NaN 0.0 NaN NaN
3031 9 6400000.0 56.70 2018-03-27 2 2.65 NaN 32.00 12 NaN False False 12.00 NaN Кудрово NaN NaN NaN NaN NaN NaN 231.0
In [40]:
data.plot(y='floors_total', kind='hist', bins=50, figsize=(10, 5), grid=True, title='Всего этажей');
No description has been provided for this image

Аномальных значений не наблюдаю

Считаю заменять пропуски медианными значениями будет неправильно, оставим пропуски как есть, почти заменим пропуски на ноль

In [41]:
# Удалим пропуски в столбце floors_total
data.dropna(subset=['floors_total'], inplace=True)
In [42]:
# Переведем значения в столбце floors_total в int

data['floors_total'] = data['floors_total'].astype('int')
data['floors_total'].head()
Out[42]:
floors_total
0 16
1 11
2 5
3 14
4 14

Обработаем столбец living_area

Столбец жилая площадь в квадратных метрах (м²) имеет - 1903 пропуска

In [43]:
data['living_area'].describe()
Out[43]:
living_area
count 21743.000000
mean 34.462781
std 22.037433
min 2.000000
25% 18.600000
50% 30.000000
75% 42.300000
max 409.700000

In [44]:
data.plot(y='living_area', kind='hist', bins=50, figsize=(10, 5), grid=True, title='жилая площадь в квадратных метрах (м²)');
No description has been provided for this image

Квартира с жилой площадью 2 м² выглядит странно. По данным на 2024 год, минимальный размер студий должен быть не меньше 28 м²

In [45]:
# Проверим сколько квартир с жилой площадью меньше 2 м²

display(data.query('living_area <=2'))
total_images last_price total_area first_day_exposition rooms ceiling_height floors_total living_area floor is_apartment studio open_plan kitchen_area balcony locality_name airports_nearest city_centers_nearest parks_around3000 parks_nearest ponds_around3000 ponds_nearest days_exposition
13915 20 6350000.0 52.0 2018-02-06 2 3.00 6 2.0 2 NaN False False 9.0 NaN Санкт-Петербург 32453.0 6521.0 0.0 NaN 1.0 1012.0 91.0
21758 0 2330000.0 23.0 2018-01-01 0 2.65 24 2.0 22 NaN True False NaN NaN посёлок Мурино NaN NaN NaN NaN NaN NaN 66.0

Природа такого явления не понятна, оставим как есть, чтобы сохранить данные

In [46]:
data[['total_area', 'living_area']].head(20)
Out[46]:
total_area living_area
0 108.00 51.00
1 40.40 18.60
2 56.00 34.30
3 159.00 NaN
4 100.00 32.00
5 30.40 14.40
6 37.30 10.60
7 71.60 NaN
8 33.16 15.43
9 61.00 43.60
10 39.60 20.30
11 44.00 31.00
12 54.00 30.00
13 42.80 27.00
14 36.00 17.00
15 39.00 20.50
16 82.00 55.60
17 36.00 16.50
18 31.00 19.40
19 121.00 76.00

Жилая площадь — это сумма площадей жилых комнат в квартире. К ним относятся спальня, гостиная, детская, кабинет. По этой причине думаю заменить пропуски половиной от общей площади квартиры.

In [47]:
# Заменяем пропуски в 'living_area' используя lambda-функцию
data['living_area'] = data['living_area'].fillna(data['total_area'].map(lambda x: x / 2))

# Проверим замену в датафрейме
data[['total_area', 'living_area']].head(20)
Out[47]:
total_area living_area
0 108.00 51.00
1 40.40 18.60
2 56.00 34.30
3 159.00 79.50
4 100.00 32.00
5 30.40 14.40
6 37.30 10.60
7 71.60 35.80
8 33.16 15.43
9 61.00 43.60
10 39.60 20.30
11 44.00 31.00
12 54.00 30.00
13 42.80 27.00
14 36.00 17.00
15 39.00 20.50
16 82.00 55.60
17 36.00 16.50
18 31.00 19.40
19 121.00 76.00

Обработаем is_apartment и kitchen_area

В столбце is_aparment 20890 пропусков и он булевой тип. Есть предположение, что люди могли просто не заполнить данный столбец, если квартира не студия. Заполнение пропусков в столбце буду выполнять отталкиваясь от столбца kitchen_area, если kitchen_area = NaN, то is_aparment = True, иное is_aparment = False. Такая замена предполагает, что пропуски в столбце is_aparment можно заменить по данным кухни, так как в студии кухня совмещена с жилой комнатой.

In [48]:
# Заполнение пропусков в is_apartment на основе kitchen_area
data.loc[data['kitchen_area'].isna(), 'is_apartment'] = True
data.loc[data['kitchen_area'].notna(), 'is_apartment'] = False

data['is_apartment'].head(20)
Out[48]:
is_apartment
0 False
1 False
2 False
3 True
4 False
5 False
6 False
7 False
8 False
9 False
10 False
11 False
12 False
13 False
14 False
15 False
16 False
17 False
18 False
19 False

In [49]:
# Заменим тип данных в столбце на bool
data['is_apartment'] = data['is_apartment'].astype('bool')

Заменим пропуски в столбце kitchen_area

In [50]:
# Для замены пропусков вычислим средний коэффициент отношения площади кухни к общей площади
kitchen_area_coeff = (data['kitchen_area'] / data['total_area']).mean()
In [51]:
# Заполняем пропуски в 'kitchen_area' на основе среднего коэффициента
data['kitchen_area'] = data['kitchen_area'].fillna(kitchen_area_coeff * data['total_area'])
data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 23613 entries, 0 to 23698
Data columns (total 22 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   total_images          23613 non-null  int64         
 1   last_price            23613 non-null  float64       
 2   total_area            23613 non-null  float64       
 3   first_day_exposition  23613 non-null  datetime64[ns]
 4   rooms                 23613 non-null  int64         
 5   ceiling_height        23613 non-null  float64       
 6   floors_total          23613 non-null  int64         
 7   living_area           23613 non-null  float64       
 8   floor                 23613 non-null  int64         
 9   is_apartment          23613 non-null  bool          
 10  studio                23613 non-null  bool          
 11  open_plan             23613 non-null  bool          
 12  kitchen_area          23613 non-null  float64       
 13  balcony               12133 non-null  float64       
 14  locality_name         23565 non-null  object        
 15  airports_nearest      18081 non-null  float64       
 16  city_centers_nearest  18104 non-null  float64       
 17  parks_around3000      18105 non-null  float64       
 18  parks_nearest         8045 non-null   float64       
 19  ponds_around3000      18105 non-null  float64       
 20  ponds_nearest         9061 non-null   float64       
 21  days_exposition       20441 non-null  float64       
dtypes: bool(3), datetime64[ns](1), float64(13), int64(4), object(1)
memory usage: 3.7+ MB

Расмотрим столбец balcony

Количество пропусков в столбце - 11519. Есть предположение, что количество пропусков зависит от наличия балкона, если он отсутствует значит люди не заполняли данный столбец.

In [52]:
# Осуществляем замену пропусков 0
data['balcony'] = data['balcony'].fillna(0)
data['balcony'].isna().sum()
Out[52]:
np.int64(0)
In [53]:
data.isna().sum()
Out[53]:
0
total_images 0
last_price 0
total_area 0
first_day_exposition 0
rooms 0
ceiling_height 0
floors_total 0
living_area 0
floor 0
is_apartment 0
studio 0
open_plan 0
kitchen_area 0
balcony 0
locality_name 48
airports_nearest 5532
city_centers_nearest 5509
parks_around3000 5508
parks_nearest 15568
ponds_around3000 5508
ponds_nearest 14552
days_exposition 3172

Обработаем столбец locality_name

Столбец имеет - 49 пропусков. Оставим все как есть

In [54]:
# Обработаем неявные дубликаты в столбце
data['locality_name'].sort_values().unique()
Out[54]:
array(['Бокситогорск', 'Волосово', 'Волхов', 'Всеволожск', 'Выборг',
       'Высоцк', 'Гатчина', 'Зеленогорск', 'Ивангород', 'Каменногорск',
       'Кингисепп', 'Кириши', 'Кировск', 'Колпино', 'Коммунар',
       'Красное Село', 'Кронштадт', 'Кудрово', 'Лодейное Поле',
       'Ломоносов', 'Луга', 'Любань', 'Мурино', 'Никольское',
       'Новая Ладога', 'Отрадное', 'Павловск', 'Петергоф', 'Пикалёво',
       'Подпорожье', 'Приморск', 'Приозерск', 'Пушкин', 'Санкт-Петербург',
       'Светогорск', 'Сертолово', 'Сестрорецк', 'Сланцы', 'Сосновый Бор',
       'Сясьстрой', 'Тихвин', 'Тосно', 'Шлиссельбург',
       'городской поселок Большая Ижора', 'городской поселок Янино-1',
       'городской посёлок Будогощь', 'городской посёлок Виллози',
       'городской посёлок Лесогорский', 'городской посёлок Мга',
       'городской посёлок Назия', 'городской посёлок Новоселье',
       'городской посёлок Павлово', 'городской посёлок Рощино',
       'городской посёлок Свирьстрой', 'городской посёлок Советский',
       'городской посёлок Фёдоровское', 'городской посёлок Янино-1',
       'деревня Агалатово', 'деревня Аро', 'деревня Батово',
       'деревня Бегуницы', 'деревня Белогорка', 'деревня Большая Вруда',
       'деревня Большая Пустомержа', 'деревня Большие Колпаны',
       'деревня Большое Рейзино', 'деревня Большой Сабск', 'деревня Бор',
       'деревня Борисова Грива', 'деревня Ваганово', 'деревня Вартемяги',
       'деревня Вахнова Кара', 'деревня Выскатка', 'деревня Гарболово',
       'деревня Глинка', 'деревня Горбунки', 'деревня Гостилицы',
       'деревня Заклинье', 'деревня Заневка', 'деревня Зимитицы',
       'деревня Извара', 'деревня Иссад', 'деревня Калитино',
       'деревня Кальтино', 'деревня Камышовка', 'деревня Каськово',
       'деревня Келози', 'деревня Кипень', 'деревня Кисельня',
       'деревня Колтуши', 'деревня Коркино', 'деревня Котлы',
       'деревня Кривко', 'деревня Кудрово', 'деревня Кузьмолово',
       'деревня Курковицы', 'деревня Куровицы', 'деревня Куттузи',
       'деревня Лаврики', 'деревня Лаголово', 'деревня Лампово',
       'деревня Лесколово', 'деревня Лопухинка', 'деревня Лупполово',
       'деревня Малая Романовка', 'деревня Малое Верево',
       'деревня Малое Карлино', 'деревня Малые Колпаны',
       'деревня Мануйлово', 'деревня Меньково', 'деревня Мины',
       'деревня Мистолово', 'деревня Ненимяки', 'деревня Нижние Осельки',
       'деревня Нижняя', 'деревня Низино', 'деревня Новое Девяткино',
       'деревня Новолисино', 'деревня Нурма', 'деревня Оржицы',
       'деревня Парицы', 'деревня Пельгора', 'деревня Пеники',
       'деревня Пижма', 'деревня Пикколово', 'деревня Пудомяги',
       'деревня Пустынка', 'деревня Пчева', 'деревня Рабитицы',
       'деревня Разбегаево', 'деревня Раздолье', 'деревня Разметелево',
       'деревня Рапполово', 'деревня Реброво', 'деревня Русско',
       'деревня Сижно', 'деревня Снегирёвка', 'деревня Старая',
       'деревня Старая Пустошь', 'деревня Старое Хинколово',
       'деревня Старополье', 'деревня Старосиверская',
       'деревня Старые Бегуницы', 'деревня Суоранда',
       'деревня Сяськелево', 'деревня Тарасово', 'деревня Терпилицы',
       'деревня Тихковицы', 'деревня Тойворово', 'деревня Торосово',
       'деревня Торошковичи', 'деревня Трубников Бор',
       'деревня Фалилеево', 'деревня Фёдоровское', 'деревня Хапо-Ое',
       'деревня Хязельки', 'деревня Чудской Бор', 'деревня Шпаньково',
       'деревня Щеглово', 'деревня Юкки', 'деревня Ялгино',
       'деревня Яльгелево', 'деревня Ям-Тесово',
       'коттеджный поселок Кивеннапа Север', 'коттеджный поселок Счастье',
       'коттеджный посёлок Лесное', 'поселок Аннино', 'поселок Барышево',
       'поселок Бугры', 'поселок Возрождение', 'поселок Войсковицы',
       'поселок Володарское', 'поселок Гаврилово', 'поселок Гарболово',
       'поселок Гладкое', 'поселок Глажево', 'поселок Глебычево',
       'поселок Гончарово', 'поселок Громово', 'поселок Дружноселье',
       'поселок Елизаветино', 'поселок Жилгородок', 'поселок Жилпосёлок',
       'поселок Житково', 'поселок Заводской', 'поселок Запорожское',
       'поселок Зимитицы', 'поселок Ильичёво', 'поселок Калитино',
       'поселок Каложицы', 'поселок Кингисеппский', 'поселок Кирпичное',
       'поселок Кобралово', 'поселок Кобринское', 'поселок Коммунары',
       'поселок Коробицыно', 'поселок Котельский',
       'поселок Красная Долина', 'поселок Красносельское',
       'поселок Лесное', 'поселок Лисий Нос', 'поселок Лукаши',
       'поселок Любань', 'поселок Мельниково', 'поселок Мичуринское',
       'поселок Молодцово', 'поселок Мурино', 'поселок Новый Свет',
       'поселок Новый Учхоз', 'поселок Оредеж',
       'поселок Пансионат Зелёный Бор', 'поселок Первомайское',
       'поселок Перово', 'поселок Петровское', 'поселок Победа',
       'поселок Поляны', 'поселок Почап', 'поселок Починок',
       'поселок Пушное', 'поселок Пчевжа', 'поселок Рабитицы',
       'поселок Романовка', 'поселок Ромашки', 'поселок Рябово',
       'поселок Севастьяново', 'поселок Селезнёво', 'поселок Сельцо',
       'поселок Семиозерье', 'поселок Семрино', 'поселок Серебрянский',
       'поселок Совхозный', 'поселок Старая Малукса',
       'поселок Стеклянный', 'поселок Сумино', 'поселок Суходолье',
       'поселок Тельмана', 'поселок Терволово', 'поселок Торковичи',
       'поселок Тёсово-4', 'поселок Углово', 'поселок Усть-Луга',
       'поселок Ушаки', 'поселок Цвелодубово', 'поселок Цвылёво',
       'поселок городского типа Большая Ижора',
       'поселок городского типа Вырица',
       'поселок городского типа Дружная Горка',
       'поселок городского типа Дубровка',
       'поселок городского типа Ефимовский',
       'поселок городского типа Кондратьево',
       'поселок городского типа Красный Бор',
       'поселок городского типа Кузьмоловский',
       'поселок городского типа Лебяжье',
       'поселок городского типа Лесогорский',
       'поселок городского типа Назия',
       'поселок городского типа Никольский',
       'поселок городского типа Приладожский',
       'поселок городского типа Рахья', 'поселок городского типа Рощино',
       'поселок городского типа Рябово',
       'поселок городского типа Синявино',
       'поселок городского типа Советский',
       'поселок городского типа Токсово',
       'поселок городского типа Форносово',
       'поселок городского типа имени Свердлова',
       'поселок станции Вещево', 'поселок станции Корнево',
       'поселок станции Лужайка', 'поселок станции Приветнинское',
       'посёлок Александровская', 'посёлок Алексеевка', 'посёлок Аннино',
       'посёлок Белоостров', 'посёлок Бугры', 'посёлок Возрождение',
       'посёлок Войскорово', 'посёлок Высокоключевой',
       'посёлок Гаврилово', 'посёлок Дзержинского', 'посёлок Жилгородок',
       'посёлок Ильичёво', 'посёлок Кикерино', 'посёлок Кобралово',
       'посёлок Коробицыно', 'посёлок Левашово', 'посёлок Ленинское',
       'посёлок Лисий Нос', 'посёлок Мельниково', 'посёлок Металлострой',
       'посёлок Мичуринское', 'посёлок Молодёжное', 'посёлок Мурино',
       'посёлок Мыза-Ивановка', 'посёлок Новогорелово',
       'посёлок Новый Свет', 'посёлок Пансионат Зелёный Бор',
       'посёлок Парголово', 'посёлок Перово', 'посёлок Песочный',
       'посёлок Петро-Славянка', 'посёлок Петровское',
       'посёлок Платформа 69-й километр', 'посёлок Плодовое',
       'посёлок Плоское', 'посёлок Победа', 'посёлок Поляны',
       'посёлок Понтонный', 'посёлок Пригородный', 'посёлок Пудость',
       'посёлок Репино', 'посёлок Ропша', 'посёлок Сапёрное',
       'посёлок Сапёрный', 'посёлок Сосново', 'посёлок Старая Малукса',
       'посёлок Стеклянный', 'посёлок Стрельна', 'посёлок Суйда',
       'посёлок Сумино', 'посёлок Тельмана', 'посёлок Терволово',
       'посёлок Торфяное', 'посёлок Усть-Ижора', 'посёлок Усть-Луга',
       'посёлок Форт Красная Горка', 'посёлок Шугозеро', 'посёлок Шушары',
       'посёлок Щеглово', 'посёлок городского типа Важины',
       'посёлок городского типа Вознесенье',
       'посёлок городского типа Вырица',
       'посёлок городского типа Красный Бор',
       'посёлок городского типа Кузнечное',
       'посёлок городского типа Кузьмоловский',
       'посёлок городского типа Лебяжье', 'посёлок городского типа Мга',
       'посёлок городского типа Павлово',
       'посёлок городского типа Рощино', 'посёлок городского типа Рябово',
       'посёлок городского типа Сиверский',
       'посёлок городского типа Тайцы', 'посёлок городского типа Токсово',
       'посёлок городского типа Ульяновка',
       'посёлок городского типа Форносово',
       'посёлок городского типа имени Морозова',
       'посёлок городского типа имени Свердлова',
       'посёлок при железнодорожной станции Вещево',
       'посёлок при железнодорожной станции Приветнинское',
       'посёлок станции Громово', 'посёлок станции Свирь',
       'садоводческое некоммерческое товарищество Лесная Поляна',
       'садовое товарищество Новая Ропша',
       'садовое товарищество Приладожский', 'садовое товарищество Рахья',
       'садовое товарищество Садко', 'село Копорье', 'село Никольское',
       'село Павлово', 'село Паша', 'село Путилово', 'село Рождествено',
       'село Русско-Высоцкое', 'село Старая Ладога', 'село Шум', nan],
      dtype=object)
In [55]:
data['locality_name'] = (data['locality_name'].str
                         .replace('ё', 'е')
                         .replace(['поселок городского типа ', 'городской поселок '], 'поселок ', regex=True))

data['locality_name'].sort_values().unique()
Out[55]:
array(['Бокситогорск', 'Волосово', 'Волхов', 'Всеволожск', 'Выборг',
       'Высоцк', 'Гатчина', 'Зеленогорск', 'Ивангород', 'Каменногорск',
       'Кингисепп', 'Кириши', 'Кировск', 'Колпино', 'Коммунар',
       'Красное Село', 'Кронштадт', 'Кудрово', 'Лодейное Поле',
       'Ломоносов', 'Луга', 'Любань', 'Мурино', 'Никольское',
       'Новая Ладога', 'Отрадное', 'Павловск', 'Петергоф', 'Пикалево',
       'Подпорожье', 'Приморск', 'Приозерск', 'Пушкин', 'Санкт-Петербург',
       'Светогорск', 'Сертолово', 'Сестрорецк', 'Сланцы', 'Сосновый Бор',
       'Сясьстрой', 'Тихвин', 'Тосно', 'Шлиссельбург',
       'деревня Агалатово', 'деревня Аро', 'деревня Батово',
       'деревня Бегуницы', 'деревня Белогорка', 'деревня Большая Вруда',
       'деревня Большая Пустомержа', 'деревня Большие Колпаны',
       'деревня Большое Рейзино', 'деревня Большой Сабск', 'деревня Бор',
       'деревня Борисова Грива', 'деревня Ваганово', 'деревня Вартемяги',
       'деревня Вахнова Кара', 'деревня Выскатка', 'деревня Гарболово',
       'деревня Глинка', 'деревня Горбунки', 'деревня Гостилицы',
       'деревня Заклинье', 'деревня Заневка', 'деревня Зимитицы',
       'деревня Извара', 'деревня Иссад', 'деревня Калитино',
       'деревня Кальтино', 'деревня Камышовка', 'деревня Каськово',
       'деревня Келози', 'деревня Кипень', 'деревня Кисельня',
       'деревня Колтуши', 'деревня Коркино', 'деревня Котлы',
       'деревня Кривко', 'деревня Кудрово', 'деревня Кузьмолово',
       'деревня Курковицы', 'деревня Куровицы', 'деревня Куттузи',
       'деревня Лаврики', 'деревня Лаголово', 'деревня Лампово',
       'деревня Лесколово', 'деревня Лопухинка', 'деревня Лупполово',
       'деревня Малая Романовка', 'деревня Малое Верево',
       'деревня Малое Карлино', 'деревня Малые Колпаны',
       'деревня Мануйлово', 'деревня Меньково', 'деревня Мины',
       'деревня Мистолово', 'деревня Ненимяки', 'деревня Нижние Осельки',
       'деревня Нижняя', 'деревня Низино', 'деревня Новое Девяткино',
       'деревня Новолисино', 'деревня Нурма', 'деревня Оржицы',
       'деревня Парицы', 'деревня Пельгора', 'деревня Пеники',
       'деревня Пижма', 'деревня Пикколово', 'деревня Пудомяги',
       'деревня Пустынка', 'деревня Пчева', 'деревня Рабитицы',
       'деревня Разбегаево', 'деревня Раздолье', 'деревня Разметелево',
       'деревня Рапполово', 'деревня Реброво', 'деревня Русско',
       'деревня Сижно', 'деревня Снегиревка', 'деревня Старая',
       'деревня Старая Пустошь', 'деревня Старое Хинколово',
       'деревня Старополье', 'деревня Старосиверская',
       'деревня Старые Бегуницы', 'деревня Суоранда',
       'деревня Сяськелево', 'деревня Тарасово', 'деревня Терпилицы',
       'деревня Тихковицы', 'деревня Тойворово', 'деревня Торосово',
       'деревня Торошковичи', 'деревня Трубников Бор',
       'деревня Фалилеево', 'деревня Федоровское', 'деревня Хапо-Ое',
       'деревня Хязельки', 'деревня Чудской Бор', 'деревня Шпаньково',
       'деревня Щеглово', 'деревня Юкки', 'деревня Ялгино',
       'деревня Яльгелево', 'деревня Ям-Тесово',
       'коттеджный поселок Кивеннапа Север', 'коттеджный поселок Лесное',
       'коттеджный поселок Счастье', 'поселок Александровская',
       'поселок Алексеевка', 'поселок Аннино', 'поселок Барышево',
       'поселок Белоостров', 'поселок Большая Ижора', 'поселок Бугры',
       'поселок Будогощь', 'поселок Важины', 'поселок Виллози',
       'поселок Вознесенье', 'поселок Возрождение', 'поселок Войсковицы',
       'поселок Войскорово', 'поселок Володарское', 'поселок Вырица',
       'поселок Высокоключевой', 'поселок Гаврилово', 'поселок Гарболово',
       'поселок Гладкое', 'поселок Глажево', 'поселок Глебычево',
       'поселок Гончарово', 'поселок Громово', 'поселок Дзержинского',
       'поселок Дружная Горка', 'поселок Дружноселье', 'поселок Дубровка',
       'поселок Елизаветино', 'поселок Ефимовский', 'поселок Жилгородок',
       'поселок Жилпоселок', 'поселок Житково', 'поселок Заводской',
       'поселок Запорожское', 'поселок Зимитицы', 'поселок Ильичево',
       'поселок Калитино', 'поселок Каложицы', 'поселок Кикерино',
       'поселок Кингисеппский', 'поселок Кирпичное', 'поселок Кобралово',
       'поселок Кобринское', 'поселок Коммунары', 'поселок Кондратьево',
       'поселок Коробицыно', 'поселок Котельский',
       'поселок Красная Долина', 'поселок Красносельское',
       'поселок Красный Бор', 'поселок Кузнечное',
       'поселок Кузьмоловский', 'поселок Лебяжье', 'поселок Левашово',
       'поселок Ленинское', 'поселок Лесное', 'поселок Лесогорский',
       'поселок Лисий Нос', 'поселок Лукаши', 'поселок Любань',
       'поселок Мга', 'поселок Мельниково', 'поселок Металлострой',
       'поселок Мичуринское', 'поселок Молодежное', 'поселок Молодцово',
       'поселок Мурино', 'поселок Мыза-Ивановка', 'поселок Назия',
       'поселок Никольский', 'поселок Новогорелово', 'поселок Новоселье',
       'поселок Новый Свет', 'поселок Новый Учхоз', 'поселок Оредеж',
       'поселок Павлово', 'поселок Пансионат Зеленый Бор',
       'поселок Парголово', 'поселок Первомайское', 'поселок Перово',
       'поселок Песочный', 'поселок Петро-Славянка', 'поселок Петровское',
       'поселок Платформа 69-й километр', 'поселок Плодовое',
       'поселок Плоское', 'поселок Победа', 'поселок Поляны',
       'поселок Понтонный', 'поселок Почап', 'поселок Починок',
       'поселок Пригородный', 'поселок Приладожский', 'поселок Пудость',
       'поселок Пушное', 'поселок Пчевжа', 'поселок Рабитицы',
       'поселок Рахья', 'поселок Репино', 'поселок Романовка',
       'поселок Ромашки', 'поселок Ропша', 'поселок Рощино',
       'поселок Рябово', 'поселок Саперное', 'поселок Саперный',
       'поселок Свирьстрой', 'поселок Севастьяново', 'поселок Селезнево',
       'поселок Сельцо', 'поселок Семиозерье', 'поселок Семрино',
       'поселок Серебрянский', 'поселок Сиверский', 'поселок Синявино',
       'поселок Советский', 'поселок Совхозный', 'поселок Сосново',
       'поселок Старая Малукса', 'поселок Стеклянный', 'поселок Стрельна',
       'поселок Суйда', 'поселок Сумино', 'поселок Суходолье',
       'поселок Тайцы', 'поселок Тельмана', 'поселок Терволово',
       'поселок Тесово-4', 'поселок Токсово', 'поселок Торковичи',
       'поселок Торфяное', 'поселок Углово', 'поселок Ульяновка',
       'поселок Усть-Ижора', 'поселок Усть-Луга', 'поселок Ушаки',
       'поселок Федоровское', 'поселок Форносово',
       'поселок Форт Красная Горка', 'поселок Цвелодубово',
       'поселок Цвылево', 'поселок Шугозеро', 'поселок Шушары',
       'поселок Щеглово', 'поселок Янино-1', 'поселок имени Морозова',
       'поселок имени Свердлова',
       'поселок при железнодорожной станции Вещево',
       'поселок при железнодорожной станции Приветнинское',
       'поселок станции Вещево', 'поселок станции Громово',
       'поселок станции Корнево', 'поселок станции Лужайка',
       'поселок станции Приветнинское', 'поселок станции Свирь',
       'садоводческое некоммерческое товарищество Лесная Поляна',
       'садовое товарищество Новая Ропша',
       'садовое товарищество Приладожский', 'садовое товарищество Рахья',
       'садовое товарищество Садко', 'село Копорье', 'село Никольское',
       'село Павлово', 'село Паша', 'село Путилово', 'село Рождествено',
       'село Русско-Высоцкое', 'село Старая Ладога', 'село Шум', nan],
      dtype=object)

Обработка 'city_centers_nearest', 'parks_around3000', 'parks_nearest', 'ponds_around3000', 'ponds_nearest', 'days_exposition' нецелесообразна из за возможности сильно исказить данные при заполнении пропусков

Проверка на наличие дубликатов

In [56]:
# Проверку осуществим методом duplicated
data.duplicated().sum()
Out[56]:
np.int64(0)

Добавление новыех столбцов¶

In [57]:
# Добавление цены квадратного метра
data['square_meter_price'] = data['last_price']/data['total_area'].round(decimals=2) # Параметр decimals отвечает за округление
In [58]:
# Определим день публикации объявления
data['week_day'] = data['first_day_exposition'].dt.weekday
In [59]:
# Определим месяц публикации объявления
data['month'] = data['first_day_exposition'].dt.month
In [60]:
# Определим год публикации объявления
data['year'] = data['first_day_exposition'].dt.year
In [61]:
# Выведем результат добавления на экран
display(data[['first_day_exposition','week_day', 'month', 'year']].head(10))
first_day_exposition week_day month year
0 2019-03-07 3 3 2019
1 2018-12-04 1 12 2018
2 2015-08-20 3 8 2015
3 2015-07-24 4 7 2015
4 2018-06-19 1 6 2018
5 2018-09-10 0 9 2018
6 2017-11-02 3 11 2017
7 2019-04-18 3 4 2019
8 2018-05-23 2 5 2018
9 2017-02-26 6 2 2017

Добавление типа этажа квартиры (значения — «первый», «последний», «другой»)

In [62]:
def floor_category(row):
    if row['floor'] == 1:
        return 'первый'
    elif row['floor'] == row['floors_total']:
        return 'последний'
    else:
        return 'другой'
data['floor_category'] = data.apply(floor_category, axis=1)
In [63]:
data[['floors_total', 'floor', 'floor_category']].head(15)
Out[63]:
floors_total floor floor_category
0 16 8 другой
1 11 1 первый
2 5 4 другой
3 14 9 другой
4 14 13 другой
5 12 5 другой
6 26 6 другой
7 24 22 другой
8 27 26 другой
9 9 7 другой
10 12 3 другой
11 5 4 другой
12 5 5 последний
13 5 5 последний
14 6 1 первый

Добавление расстояния до центра города в километрах

In [64]:
data['city_centers_nearest_km'] = (data['city_centers_nearest'] / 1000).round(0)
In [65]:
data[['city_centers_nearest', 'city_centers_nearest_km']].head(10)
Out[65]:
city_centers_nearest city_centers_nearest_km
0 16028.0 16.0
1 18603.0 19.0
2 13933.0 14.0
3 6800.0 7.0
4 8098.0 8.0
5 NaN NaN
6 19143.0 19.0
7 11634.0 12.0
8 NaN NaN
9 15008.0 15.0

Исследовательский анализ данных¶

Выполним базовую проверку данных, ответим на вопросы:

  • Сколько всего объявлений?
  • Какие населенные пункты встречаются в базе данных?
  • Какая временная выборка в базе данных?
  • Какая средняя стоимость объекта недвижимости в населенном пункте?
  • В каком населенном пункте больше всего объявлений?
In [66]:
# Сколько всего объявлений?
data.shape[0]
Out[66]:
23613
In [67]:
# Какие насеянные пункты встречаются в датафрейме можно определить методом unique()
data['locality_name'].sort_values().unique()
Out[67]:
array(['Бокситогорск', 'Волосово', 'Волхов', 'Всеволожск', 'Выборг',
       'Высоцк', 'Гатчина', 'Зеленогорск', 'Ивангород', 'Каменногорск',
       'Кингисепп', 'Кириши', 'Кировск', 'Колпино', 'Коммунар',
       'Красное Село', 'Кронштадт', 'Кудрово', 'Лодейное Поле',
       'Ломоносов', 'Луга', 'Любань', 'Мурино', 'Никольское',
       'Новая Ладога', 'Отрадное', 'Павловск', 'Петергоф', 'Пикалево',
       'Подпорожье', 'Приморск', 'Приозерск', 'Пушкин', 'Санкт-Петербург',
       'Светогорск', 'Сертолово', 'Сестрорецк', 'Сланцы', 'Сосновый Бор',
       'Сясьстрой', 'Тихвин', 'Тосно', 'Шлиссельбург',
       'деревня Агалатово', 'деревня Аро', 'деревня Батово',
       'деревня Бегуницы', 'деревня Белогорка', 'деревня Большая Вруда',
       'деревня Большая Пустомержа', 'деревня Большие Колпаны',
       'деревня Большое Рейзино', 'деревня Большой Сабск', 'деревня Бор',
       'деревня Борисова Грива', 'деревня Ваганово', 'деревня Вартемяги',
       'деревня Вахнова Кара', 'деревня Выскатка', 'деревня Гарболово',
       'деревня Глинка', 'деревня Горбунки', 'деревня Гостилицы',
       'деревня Заклинье', 'деревня Заневка', 'деревня Зимитицы',
       'деревня Извара', 'деревня Иссад', 'деревня Калитино',
       'деревня Кальтино', 'деревня Камышовка', 'деревня Каськово',
       'деревня Келози', 'деревня Кипень', 'деревня Кисельня',
       'деревня Колтуши', 'деревня Коркино', 'деревня Котлы',
       'деревня Кривко', 'деревня Кудрово', 'деревня Кузьмолово',
       'деревня Курковицы', 'деревня Куровицы', 'деревня Куттузи',
       'деревня Лаврики', 'деревня Лаголово', 'деревня Лампово',
       'деревня Лесколово', 'деревня Лопухинка', 'деревня Лупполово',
       'деревня Малая Романовка', 'деревня Малое Верево',
       'деревня Малое Карлино', 'деревня Малые Колпаны',
       'деревня Мануйлово', 'деревня Меньково', 'деревня Мины',
       'деревня Мистолово', 'деревня Ненимяки', 'деревня Нижние Осельки',
       'деревня Нижняя', 'деревня Низино', 'деревня Новое Девяткино',
       'деревня Новолисино', 'деревня Нурма', 'деревня Оржицы',
       'деревня Парицы', 'деревня Пельгора', 'деревня Пеники',
       'деревня Пижма', 'деревня Пикколово', 'деревня Пудомяги',
       'деревня Пустынка', 'деревня Пчева', 'деревня Рабитицы',
       'деревня Разбегаево', 'деревня Раздолье', 'деревня Разметелево',
       'деревня Рапполово', 'деревня Реброво', 'деревня Русско',
       'деревня Сижно', 'деревня Снегиревка', 'деревня Старая',
       'деревня Старая Пустошь', 'деревня Старое Хинколово',
       'деревня Старополье', 'деревня Старосиверская',
       'деревня Старые Бегуницы', 'деревня Суоранда',
       'деревня Сяськелево', 'деревня Тарасово', 'деревня Терпилицы',
       'деревня Тихковицы', 'деревня Тойворово', 'деревня Торосово',
       'деревня Торошковичи', 'деревня Трубников Бор',
       'деревня Фалилеево', 'деревня Федоровское', 'деревня Хапо-Ое',
       'деревня Хязельки', 'деревня Чудской Бор', 'деревня Шпаньково',
       'деревня Щеглово', 'деревня Юкки', 'деревня Ялгино',
       'деревня Яльгелево', 'деревня Ям-Тесово',
       'коттеджный поселок Кивеннапа Север', 'коттеджный поселок Лесное',
       'коттеджный поселок Счастье', 'поселок Александровская',
       'поселок Алексеевка', 'поселок Аннино', 'поселок Барышево',
       'поселок Белоостров', 'поселок Большая Ижора', 'поселок Бугры',
       'поселок Будогощь', 'поселок Важины', 'поселок Виллози',
       'поселок Вознесенье', 'поселок Возрождение', 'поселок Войсковицы',
       'поселок Войскорово', 'поселок Володарское', 'поселок Вырица',
       'поселок Высокоключевой', 'поселок Гаврилово', 'поселок Гарболово',
       'поселок Гладкое', 'поселок Глажево', 'поселок Глебычево',
       'поселок Гончарово', 'поселок Громово', 'поселок Дзержинского',
       'поселок Дружная Горка', 'поселок Дружноселье', 'поселок Дубровка',
       'поселок Елизаветино', 'поселок Ефимовский', 'поселок Жилгородок',
       'поселок Жилпоселок', 'поселок Житково', 'поселок Заводской',
       'поселок Запорожское', 'поселок Зимитицы', 'поселок Ильичево',
       'поселок Калитино', 'поселок Каложицы', 'поселок Кикерино',
       'поселок Кингисеппский', 'поселок Кирпичное', 'поселок Кобралово',
       'поселок Кобринское', 'поселок Коммунары', 'поселок Кондратьево',
       'поселок Коробицыно', 'поселок Котельский',
       'поселок Красная Долина', 'поселок Красносельское',
       'поселок Красный Бор', 'поселок Кузнечное',
       'поселок Кузьмоловский', 'поселок Лебяжье', 'поселок Левашово',
       'поселок Ленинское', 'поселок Лесное', 'поселок Лесогорский',
       'поселок Лисий Нос', 'поселок Лукаши', 'поселок Любань',
       'поселок Мга', 'поселок Мельниково', 'поселок Металлострой',
       'поселок Мичуринское', 'поселок Молодежное', 'поселок Молодцово',
       'поселок Мурино', 'поселок Мыза-Ивановка', 'поселок Назия',
       'поселок Никольский', 'поселок Новогорелово', 'поселок Новоселье',
       'поселок Новый Свет', 'поселок Новый Учхоз', 'поселок Оредеж',
       'поселок Павлово', 'поселок Пансионат Зеленый Бор',
       'поселок Парголово', 'поселок Первомайское', 'поселок Перово',
       'поселок Песочный', 'поселок Петро-Славянка', 'поселок Петровское',
       'поселок Платформа 69-й километр', 'поселок Плодовое',
       'поселок Плоское', 'поселок Победа', 'поселок Поляны',
       'поселок Понтонный', 'поселок Почап', 'поселок Починок',
       'поселок Пригородный', 'поселок Приладожский', 'поселок Пудость',
       'поселок Пушное', 'поселок Пчевжа', 'поселок Рабитицы',
       'поселок Рахья', 'поселок Репино', 'поселок Романовка',
       'поселок Ромашки', 'поселок Ропша', 'поселок Рощино',
       'поселок Рябово', 'поселок Саперное', 'поселок Саперный',
       'поселок Свирьстрой', 'поселок Севастьяново', 'поселок Селезнево',
       'поселок Сельцо', 'поселок Семиозерье', 'поселок Семрино',
       'поселок Серебрянский', 'поселок Сиверский', 'поселок Синявино',
       'поселок Советский', 'поселок Совхозный', 'поселок Сосново',
       'поселок Старая Малукса', 'поселок Стеклянный', 'поселок Стрельна',
       'поселок Суйда', 'поселок Сумино', 'поселок Суходолье',
       'поселок Тайцы', 'поселок Тельмана', 'поселок Терволово',
       'поселок Тесово-4', 'поселок Токсово', 'поселок Торковичи',
       'поселок Торфяное', 'поселок Углово', 'поселок Ульяновка',
       'поселок Усть-Ижора', 'поселок Усть-Луга', 'поселок Ушаки',
       'поселок Федоровское', 'поселок Форносово',
       'поселок Форт Красная Горка', 'поселок Цвелодубово',
       'поселок Цвылево', 'поселок Шугозеро', 'поселок Шушары',
       'поселок Щеглово', 'поселок Янино-1', 'поселок имени Морозова',
       'поселок имени Свердлова',
       'поселок при железнодорожной станции Вещево',
       'поселок при железнодорожной станции Приветнинское',
       'поселок станции Вещево', 'поселок станции Громово',
       'поселок станции Корнево', 'поселок станции Лужайка',
       'поселок станции Приветнинское', 'поселок станции Свирь',
       'садоводческое некоммерческое товарищество Лесная Поляна',
       'садовое товарищество Новая Ропша',
       'садовое товарищество Приладожский', 'садовое товарищество Рахья',
       'садовое товарищество Садко', 'село Копорье', 'село Никольское',
       'село Павлово', 'село Паша', 'село Путилово', 'село Рождествено',
       'село Русско-Высоцкое', 'село Старая Ладога', 'село Шум', nan],
      dtype=object)
In [68]:
# Какая временная выборка в базе данных?
print(f"Диапазон дат: от {data['first_day_exposition'].min()} до {data['first_day_exposition'].max()}")

Диапазон дат: от 2014-11-27 00:00:00 до 2019-05-03 00:00:00
In [69]:
# Какая средняя стоимость объекта недвижимости в населенном пункте?
average_price = data.groupby('locality_name')['last_price'].mean().round(decimals=2)
average_price.head(40)
Out[69]:
last_price
locality_name
Бокситогорск 846187.50
Волосово 1977777.78
Волхов 1739909.01
Всеволожск 3789067.75
Выборг 3250983.12
Высоцк 1810000.00
Гатчина 3488336.68
Зеленогорск 6288337.50
Ивангород 1486428.54
Каменногорск 1367692.31
Кингисепп 2461642.31
Кириши 1834624.00
Кировск 2749464.27
Колпино 3964021.44
Коммунар 2770921.35
Красное Село 3821852.27
Кронштадт 4132364.57
Кудрово 4566009.01
Лодейное Поле 1679000.00
Ломоносов 3662879.70
Луга 2156785.71
Любань 1677500.00
Мурино 4105287.35
Никольское 2363567.31
Новая Ладога 1978571.43
Отрадное 2837196.75
Павловск 4929710.53
Петергоф 4423840.80
Пикалево 1006666.67
Подпорожье 1420526.32
Приморск 2122500.00
Приозерск 1995075.76
Пушкин 6469035.95
Санкт-Петербург 8064895.30
Светогорск 1961818.18
Сертолово 3652432.39
Сестрорецк 6341722.08
Сланцы 873125.00
Сосновый Бор 3908129.20
Сясьстрой 1646250.00

In [70]:
# В каком населенном пункте больше всего объявлений?

print(data['locality_name'].value_counts().head(1))

locality_name
Санкт-Петербург    15651
Name: count, dtype: int64

Изучим и опишем параметры:

  • общая площадь;
  • жилая площадь;
  • площадь кухни;
  • цена объекта;
  • количество комнат;
  • высота потолков;
  • тип этажа квартиры («первый», «последний», «другой»);
  • общее количество этажей в доме;
  • расстояние до центра города в метрах;
  • расстояние до ближайшего парка.

Общая площадь

In [71]:
# Общая площадь
data.plot(y='total_area', kind='hist', bins=100, figsize=(10, 5), grid=True, title='Общая площадь в м²')
# Задание названия оси абсцисс
plt.xlabel('Quadrature')
# Показать график
plt.show()

data.plot(y='total_area', kind='hist', bins=100, range=(0, 200), figsize=(10, 5),
          grid=True, title='Общая площадь в м²')
# Задание названия оси абсцисс
plt.xlabel('Quadrature')
# Показать график
plt.show()

data['total_area'].describe()
No description has been provided for this image
No description has been provided for this image
Out[71]:
total_area
count 23613.000000
mean 60.340678
std 35.648988
min 12.000000
25% 40.000000
50% 52.000000
75% 69.800000
max 900.000000

In [72]:
# Подсчет количества квартир с общей площадью 257 м²
count_total_area = data.query('total_area >= 257').shape[0]
print(f'Количество равно - {count_total_area}')


# Построим график, исключив из анализа строки, где общая площадь составляет 257 м² или больше.
data.query('total_area <= 257', inplace=True)

# Проверим замену
display(data['total_area'].describe().round())

data.plot(y='total_area', kind='hist', bins=50, figsize=(15, 5),
          grid=True, title='Общая площадь в м²')

# Задание названия оси абсцисс
plt.xlabel('Quadrature')

# Показать график
plt.show()

Количество равно - 99
total_area
count 23515.0
mean 59.0
std 29.0
min 12.0
25% 40.0
50% 52.0
75% 69.0
max 257.0

No description has been provided for this image

По полученным графикам можно сделать вывод, что самый большой показатель площади квартир находится в пределах от ~30 м² (скорее всего это студии) до ~45 м². Недвижимость с показателем значения больше 125 м² не такая популярная на площадке и идет на убывание.

Жилая площадь

In [73]:
# Жилая площадь
data.plot(y='living_area', kind='hist', bins=100, figsize=(15, 5), grid=True, title='Жилая площадь в м²')
# Задание названия оси абсцисс
plt.xlabel('Quadrature')
# Показать график
plt.show()

data.plot(y='living_area', kind='hist', bins=100, range=(0, 200), figsize=(15, 5),
          grid=True, title='Жилая площадь в м²')
# Задание названия оси абсцисс
plt.xlabel('Quadrature')
# Показать график
plt.show()

data['living_area'].describe()
No description has been provided for this image
No description has been provided for this image
Out[73]:
living_area
count 23515.000000
mean 33.554271
std 18.936105
min 2.000000
25% 18.800000
50% 30.000000
75% 42.000000
max 220.000000

In [74]:
# Подсчет количества квартир с жилой площадью 132 м²

count_living_area = data.query('living_area >= 132').shape[0]
print(f'Количество равно - {count_living_area}')


# Построим график, исключив из анализа строки, где жилая площадь составляет 140 м² или больше.
data.query('living_area <= 132', inplace=True)

# Проверим замену
display(data['living_area'].describe().round())

data.plot(y='living_area', kind='hist', bins=50, figsize=(15, 5),
          grid=True, title='Жилая площадь в м²')

# Задание названия оси абсцисс
plt.xlabel('Quadrature')

# Показать график
plt.show()

Количество равно - 96
living_area
count 23420.0
mean 33.0
std 17.0
min 2.0
25% 19.0
50% 30.0
75% 42.0
max 132.0

No description has been provided for this image

Наибольшая доля приходится на жилые помещения с площадью от 16 до 20 м². Затем наблюдается неожиданное снижение доли для помещений площадью от 22 примерно до 30 м². При этом разница между медианой и средним значением незначительна.

Площадь кухни

In [75]:
# Площадь кухни

data.plot(y='kitchen_area', kind='hist', bins=100, figsize=(10, 5), grid=True, title='Площадь кухни в м²')

# Задание названия оси абсцисс
plt.xlabel('Quadrature')

# Показать график
plt.show()

data.plot(y='kitchen_area', kind='hist', bins=100, range=(0, 60), figsize=(10, 5),
          grid=True, title='Площадь кухни в м²')

# Задание названия оси абсцисс
plt.xlabel('Quadrature')

# Показать график
plt.show()

data['kitchen_area'].describe()
No description has been provided for this image
No description has been provided for this image
Out[75]:
kitchen_area
count 23420.000000
mean 10.383626
std 5.501995
min 1.300000
25% 7.000000
50% 9.000000
75% 12.000000
max 107.000000

In [76]:
# Подсчет количества квартир с площадью кухни от 42 м²
count_kitchen_area = data.query('kitchen_area >= 42').shape[0]
print(f'Количество равно - {count_kitchen_area}')


# Построим график, исключив из анализа строки, где площадь кухни составляет 42 м² или больше.
data.query('kitchen_area <= 42', inplace=True)

# Проверим замену
display(data['kitchen_area'].describe().round())

data.plot(y='kitchen_area', kind='hist', bins=50, figsize=(15, 5),
          grid=True, title='Площадь кухни в м²')
# Задание названия оси абсцисс
plt.xlabel('Quadrature')
# Показать график
plt.show()

Количество равно - 91
kitchen_area
count 23331.0
mean 10.0
std 5.0
min 1.0
25% 7.0
50% 9.0
75% 12.0
max 42.0

No description has been provided for this image

По показателям площади кухни можно сделать вывод, что самая популярная квадратура для кухни это от 6 до 11 м².

Цена объекта

In [77]:
# Цена объекта
data.plot(y='last_price', kind='hist', bins=100, figsize=(10, 5), grid=True, title='Цена объекта в ₽')
# Задание названия оси абсцисс
plt.xlabel('Price M.₽')
# Показать график
plt.show()

data.plot(y='last_price', kind='hist', bins=100, range=(0, 20000000), figsize=(10, 5),
          grid=True, title='Цена объекта в ₽')
# Задание названия оси абсцисс
plt.xlabel('Price M.₽')
# Показать график
plt.show()

data['last_price'].describe().round()
No description has been provided for this image
No description has been provided for this image
Out[77]:
last_price
count 23331.0
mean 5986743.0
std 6182101.0
min 12190.0
25% 3400000.0
50% 4600000.0
75% 6650000.0
max 330000000.0

In [78]:
pd.qcut(data['last_price'], q=12)
Out[78]:
last_price
0 (11300000.0, 330000000.0]
1 (2980949.0, 3400000.0]
2 (5100000.0, 5750000.0]
3 (11300000.0, 330000000.0]
4 (8045000.0, 11300000.0]
5 (2300000.0, 2980949.0]
6 (3400000.0, 3800000.0]
7 (6650000.0, 8045000.0]
8 (2300000.0, 2980949.0]
9 (5100000.0, 5750000.0]
10 (4600000.0, 5100000.0]
11 (2980949.0, 3400000.0]
12 (3800000.0, 4200000.0]
13 (3400000.0, 3800000.0]
14 (4200000.0, 4600000.0]
15 (4600000.0, 5100000.0]
16 (6650000.0, 8045000.0]
17 (3800000.0, 4200000.0]
18 (2980949.0, 3400000.0]
19 (11300000.0, 330000000.0]
20 (5750000.0, 6650000.0]
21 (2980949.0, 3400000.0]
22 (4600000.0, 5100000.0]
23 (2300000.0, 2980949.0]
24 (5750000.0, 6650000.0]
25 (6650000.0, 8045000.0]
26 (3800000.0, 4200000.0]
27 (6650000.0, 8045000.0]
28 (3800000.0, 4200000.0]
29 (8045000.0, 11300000.0]
30 (12189.999, 2300000.0]
31 (6650000.0, 8045000.0]
32 (4600000.0, 5100000.0]
33 (4600000.0, 5100000.0]
34 (2980949.0, 3400000.0]
35 (11300000.0, 330000000.0]
36 (3400000.0, 3800000.0]
37 (12189.999, 2300000.0]
38 (2980949.0, 3400000.0]
39 (5100000.0, 5750000.0]
40 (3400000.0, 3800000.0]
41 (6650000.0, 8045000.0]
42 (11300000.0, 330000000.0]
43 (8045000.0, 11300000.0]
44 (5100000.0, 5750000.0]
45 (5100000.0, 5750000.0]
46 (5750000.0, 6650000.0]
47 (3400000.0, 3800000.0]
48 (3400000.0, 3800000.0]
49 (2980949.0, 3400000.0]
50 (4200000.0, 4600000.0]
52 (11300000.0, 330000000.0]
53 (4200000.0, 4600000.0]
54 (2980949.0, 3400000.0]
55 (6650000.0, 8045000.0]
56 (4200000.0, 4600000.0]
57 (2980949.0, 3400000.0]
58 (3800000.0, 4200000.0]
59 (5750000.0, 6650000.0]
60 (2300000.0, 2980949.0]
61 (6650000.0, 8045000.0]
62 (4600000.0, 5100000.0]
63 (11300000.0, 330000000.0]
64 (2980949.0, 3400000.0]
65 (4200000.0, 4600000.0]
66 (3400000.0, 3800000.0]
67 (2300000.0, 2980949.0]
68 (3800000.0, 4200000.0]
69 (3800000.0, 4200000.0]
70 (2980949.0, 3400000.0]
71 (4600000.0, 5100000.0]
72 (4200000.0, 4600000.0]
73 (4600000.0, 5100000.0]
74 (2300000.0, 2980949.0]
75 (4600000.0, 5100000.0]
76 (2300000.0, 2980949.0]
77 (4200000.0, 4600000.0]
78 (4200000.0, 4600000.0]
79 (2300000.0, 2980949.0]
80 (3800000.0, 4200000.0]
81 (8045000.0, 11300000.0]
82 (4200000.0, 4600000.0]
83 (3800000.0, 4200000.0]
84 (2300000.0, 2980949.0]
85 (5100000.0, 5750000.0]
86 (4200000.0, 4600000.0]
87 (4600000.0, 5100000.0]
88 (12189.999, 2300000.0]
89 (2300000.0, 2980949.0]
90 (5750000.0, 6650000.0]
91 (12189.999, 2300000.0]
92 (3800000.0, 4200000.0]
93 (3400000.0, 3800000.0]
94 (3400000.0, 3800000.0]
95 (5100000.0, 5750000.0]
96 (8045000.0, 11300000.0]
97 (4600000.0, 5100000.0]
98 (11300000.0, 330000000.0]
99 (8045000.0, 11300000.0]
100 (3400000.0, 3800000.0]
101 (2980949.0, 3400000.0]
102 (3400000.0, 3800000.0]
103 (4600000.0, 5100000.0]
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1743 (2300000.0, 2980949.0]
1744 (4200000.0, 4600000.0]
1745 (4200000.0, 4600000.0]
1746 (12189.999, 2300000.0]
1747 (4200000.0, 4600000.0]
1748 (11300000.0, 330000000.0]
1749 (5100000.0, 5750000.0]
1750 (2300000.0, 2980949.0]
1751 (12189.999, 2300000.0]
1752 (3800000.0, 4200000.0]
1753 (12189.999, 2300000.0]
1754 (3400000.0, 3800000.0]
1755 (5750000.0, 6650000.0]
1756 (2300000.0, 2980949.0]
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1759 (6650000.0, 8045000.0]
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1769 (5100000.0, 5750000.0]
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1771 (4200000.0, 4600000.0]
1772 (5750000.0, 6650000.0]
1773 (6650000.0, 8045000.0]
1774 (4200000.0, 4600000.0]
1775 (2300000.0, 2980949.0]
1776 (8045000.0, 11300000.0]
1777 (11300000.0, 330000000.0]
1778 (2300000.0, 2980949.0]
1779 (6650000.0, 8045000.0]
1780 (3400000.0, 3800000.0]
1781 (12189.999, 2300000.0]
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1783 (5100000.0, 5750000.0]
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1785 (3800000.0, 4200000.0]
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1787 (12189.999, 2300000.0]
1788 (5750000.0, 6650000.0]
1789 (2300000.0, 2980949.0]
1790 (3800000.0, 4200000.0]
1791 (5100000.0, 5750000.0]
1792 (2980949.0, 3400000.0]
1793 (8045000.0, 11300000.0]
1794 (12189.999, 2300000.0]
1795 (12189.999, 2300000.0]
1796 (2300000.0, 2980949.0]
1797 (3400000.0, 3800000.0]
1798 (8045000.0, 11300000.0]
1799 (12189.999, 2300000.0]
1800 (11300000.0, 330000000.0]
1801 (2300000.0, 2980949.0]
1802 (2980949.0, 3400000.0]
1803 (8045000.0, 11300000.0]
1804 (11300000.0, 330000000.0]
1805 (2300000.0, 2980949.0]
1806 (2980949.0, 3400000.0]
1807 (5100000.0, 5750000.0]
1809 (5100000.0, 5750000.0]
1810 (11300000.0, 330000000.0]
1811 (8045000.0, 11300000.0]
1812 (4600000.0, 5100000.0]
1813 (11300000.0, 330000000.0]
1814 (5750000.0, 6650000.0]
1815 (3400000.0, 3800000.0]
1816 (12189.999, 2300000.0]
1817 (6650000.0, 8045000.0]
1818 (6650000.0, 8045000.0]
1819 (4200000.0, 4600000.0]
1820 (5100000.0, 5750000.0]
1821 (12189.999, 2300000.0]
1822 (6650000.0, 8045000.0]
1823 (3800000.0, 4200000.0]
1824 (4200000.0, 4600000.0]
1825 (4600000.0, 5100000.0]
1826 (12189.999, 2300000.0]
1827 (4600000.0, 5100000.0]
1828 (3800000.0, 4200000.0]
1829 (11300000.0, 330000000.0]
1830 (8045000.0, 11300000.0]
1831 (3800000.0, 4200000.0]
1832 (2980949.0, 3400000.0]
1833 (3400000.0, 3800000.0]
1834 (12189.999, 2300000.0]
1835 (5100000.0, 5750000.0]
1836 (8045000.0, 11300000.0]
1837 (4600000.0, 5100000.0]
1838 (3400000.0, 3800000.0]
1839 (3800000.0, 4200000.0]
1840 (8045000.0, 11300000.0]
1841 (6650000.0, 8045000.0]
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1843 (5100000.0, 5750000.0]
1844 (3400000.0, 3800000.0]
1845 (4200000.0, 4600000.0]
1846 (11300000.0, 330000000.0]
1847 (3400000.0, 3800000.0]
1848 (3400000.0, 3800000.0]
1849 (5750000.0, 6650000.0]
1850 (5750000.0, 6650000.0]
1851 (8045000.0, 11300000.0]
1852 (5750000.0, 6650000.0]
1853 (2980949.0, 3400000.0]
1854 (5750000.0, 6650000.0]
1855 (3400000.0, 3800000.0]
1856 (2300000.0, 2980949.0]
1857 (2300000.0, 2980949.0]
1858 (2300000.0, 2980949.0]
1859 (6650000.0, 8045000.0]
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1867 (8045000.0, 11300000.0]
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1871 (5100000.0, 5750000.0]
1872 (2980949.0, 3400000.0]
1873 (2300000.0, 2980949.0]
1874 (12189.999, 2300000.0]
1875 (8045000.0, 11300000.0]
1876 (11300000.0, 330000000.0]
1877 (5100000.0, 5750000.0]
1878 (5750000.0, 6650000.0]
1879 (3400000.0, 3800000.0]
1880 (11300000.0, 330000000.0]
1881 (4200000.0, 4600000.0]
1882 (8045000.0, 11300000.0]
1883 (8045000.0, 11300000.0]
1884 (5750000.0, 6650000.0]
1885 (11300000.0, 330000000.0]
1886 (5100000.0, 5750000.0]
1887 (2980949.0, 3400000.0]
1888 (4200000.0, 4600000.0]
1889 (4600000.0, 5100000.0]
1890 (2980949.0, 3400000.0]
1891 (4200000.0, 4600000.0]
1892 (5750000.0, 6650000.0]
1893 (6650000.0, 8045000.0]
1894 (12189.999, 2300000.0]
1895 (8045000.0, 11300000.0]
1896 (4600000.0, 5100000.0]
1897 (3800000.0, 4200000.0]
1898 (3800000.0, 4200000.0]
1899 (3800000.0, 4200000.0]
1900 (2980949.0, 3400000.0]
1901 (2300000.0, 2980949.0]
1902 (2300000.0, 2980949.0]
1903 (6650000.0, 8045000.0]
1904 (5100000.0, 5750000.0]
1905 (4200000.0, 4600000.0]
1906 (3400000.0, 3800000.0]
1907 (4200000.0, 4600000.0]
1908 (6650000.0, 8045000.0]
1909 (2980949.0, 3400000.0]
1910 (8045000.0, 11300000.0]
1911 (3800000.0, 4200000.0]
1912 (6650000.0, 8045000.0]
1913 (12189.999, 2300000.0]
1914 (3800000.0, 4200000.0]
1915 (11300000.0, 330000000.0]
1916 (11300000.0, 330000000.0]
1917 (8045000.0, 11300000.0]
1918 (2980949.0, 3400000.0]
1919 (4200000.0, 4600000.0]
1920 (5750000.0, 6650000.0]
1921 (2980949.0, 3400000.0]
1922 (4200000.0, 4600000.0]
1923 (2300000.0, 2980949.0]
1924 (4200000.0, 4600000.0]
1925 (5750000.0, 6650000.0]
1926 (4200000.0, 4600000.0]
1927 (6650000.0, 8045000.0]
1929 (5750000.0, 6650000.0]
1931 (5750000.0, 6650000.0]
1932 (2300000.0, 2980949.0]
1933 (4600000.0, 5100000.0]
1934 (8045000.0, 11300000.0]
1935 (6650000.0, 8045000.0]
1936 (5100000.0, 5750000.0]
1937 (3400000.0, 3800000.0]
1938 (8045000.0, 11300000.0]
1939 (3400000.0, 3800000.0]
1940 (2980949.0, 3400000.0]
1941 (4600000.0, 5100000.0]
1942 (4600000.0, 5100000.0]
1943 (11300000.0, 330000000.0]
1944 (11300000.0, 330000000.0]
1945 (3800000.0, 4200000.0]
1946 (5750000.0, 6650000.0]
1947 (12189.999, 2300000.0]
1948 (4600000.0, 5100000.0]
1949 (12189.999, 2300000.0]
1950 (2300000.0, 2980949.0]
1951 (11300000.0, 330000000.0]
1952 (3800000.0, 4200000.0]
1953 (4600000.0, 5100000.0]
1954 (4600000.0, 5100000.0]
1955 (3800000.0, 4200000.0]
1956 (4200000.0, 4600000.0]
1957 (2980949.0, 3400000.0]
1958 (2300000.0, 2980949.0]
1959 (4200000.0, 4600000.0]
1960 (4200000.0, 4600000.0]
1961 (3800000.0, 4200000.0]
1962 (2980949.0, 3400000.0]
1963 (3400000.0, 3800000.0]
1964 (2980949.0, 3400000.0]
1965 (2980949.0, 3400000.0]
1966 (11300000.0, 330000000.0]
1967 (5100000.0, 5750000.0]
1968 (5100000.0, 5750000.0]
1969 (2980949.0, 3400000.0]
1970 (4600000.0, 5100000.0]
1971 (4200000.0, 4600000.0]
1972 (4600000.0, 5100000.0]
1973 (4600000.0, 5100000.0]
1974 (3800000.0, 4200000.0]
1975 (6650000.0, 8045000.0]
1976 (6650000.0, 8045000.0]
1977 (8045000.0, 11300000.0]
1978 (5100000.0, 5750000.0]
1979 (6650000.0, 8045000.0]
1980 (11300000.0, 330000000.0]
1981 (12189.999, 2300000.0]
1982 (12189.999, 2300000.0]
1983 (12189.999, 2300000.0]
1984 (5750000.0, 6650000.0]
1985 (12189.999, 2300000.0]
1986 (4600000.0, 5100000.0]
1987 (2300000.0, 2980949.0]
1988 (3400000.0, 3800000.0]
1989 (11300000.0, 330000000.0]
1990 (2980949.0, 3400000.0]
1991 (4200000.0, 4600000.0]
1992 (3800000.0, 4200000.0]
1993 (5750000.0, 6650000.0]
1994 (8045000.0, 11300000.0]
1995 (5100000.0, 5750000.0]
1996 (5750000.0, 6650000.0]
1997 (5100000.0, 5750000.0]
1998 (3400000.0, 3800000.0]
1999 (2300000.0, 2980949.0]
2000 (3800000.0, 4200000.0]
2001 (12189.999, 2300000.0]
2002 (3400000.0, 3800000.0]
2003 (11300000.0, 330000000.0]
2004 (5100000.0, 5750000.0]
2005 (4600000.0, 5100000.0]
2006 (8045000.0, 11300000.0]
2007 (4200000.0, 4600000.0]
2008 (3800000.0, 4200000.0]
2009 (3800000.0, 4200000.0]
2010 (4600000.0, 5100000.0]
2011 (5750000.0, 6650000.0]
2012 (8045000.0, 11300000.0]
2013 (3400000.0, 3800000.0]
2014 (5100000.0, 5750000.0]
2015 (5750000.0, 6650000.0]
2016 (2980949.0, 3400000.0]
2017 (8045000.0, 11300000.0]
2018 (12189.999, 2300000.0]
2019 (2300000.0, 2980949.0]
2020 (4200000.0, 4600000.0]
2021 (8045000.0, 11300000.0]
2022 (2300000.0, 2980949.0]
2023 (11300000.0, 330000000.0]
2024 (3400000.0, 3800000.0]
2025 (5100000.0, 5750000.0]
2026 (2300000.0, 2980949.0]
2027 (3800000.0, 4200000.0]
2028 (5750000.0, 6650000.0]
2029 (5750000.0, 6650000.0]
2030 (11300000.0, 330000000.0]
2031 (6650000.0, 8045000.0]
2032 (4200000.0, 4600000.0]
2033 (5100000.0, 5750000.0]
2034 (4200000.0, 4600000.0]
2035 (4200000.0, 4600000.0]
2036 (5750000.0, 6650000.0]
2037 (6650000.0, 8045000.0]
2038 (3400000.0, 3800000.0]
2039 (11300000.0, 330000000.0]
2040 (2980949.0, 3400000.0]
2041 (5100000.0, 5750000.0]
2042 (6650000.0, 8045000.0]
2043 (4600000.0, 5100000.0]
2044 (11300000.0, 330000000.0]
2045 (4600000.0, 5100000.0]
2046 (11300000.0, 330000000.0]
2047 (3800000.0, 4200000.0]
2048 (8045000.0, 11300000.0]
2049 (12189.999, 2300000.0]
2050 (3400000.0, 3800000.0]
2051 (4600000.0, 5100000.0]
2052 (12189.999, 2300000.0]
2053 (5100000.0, 5750000.0]
2054 (12189.999, 2300000.0]
2055 (12189.999, 2300000.0]
2056 (5750000.0, 6650000.0]
2057 (2980949.0, 3400000.0]
2058 (3800000.0, 4200000.0]
2059 (5750000.0, 6650000.0]
2060 (12189.999, 2300000.0]
2061 (3800000.0, 4200000.0]
2062 (3800000.0, 4200000.0]
2063 (12189.999, 2300000.0]
2064 (4200000.0, 4600000.0]
2065 (8045000.0, 11300000.0]
2066 (2300000.0, 2980949.0]
2067 (2300000.0, 2980949.0]
2068 (5100000.0, 5750000.0]
2070 (4600000.0, 5100000.0]
2071 (6650000.0, 8045000.0]
2072 (3400000.0, 3800000.0]
2073 (11300000.0, 330000000.0]
2074 (12189.999, 2300000.0]
2075 (3400000.0, 3800000.0]
2076 (5100000.0, 5750000.0]
2077 (4200000.0, 4600000.0]
2078 (5750000.0, 6650000.0]
2079 (3800000.0, 4200000.0]
2080 (4600000.0, 5100000.0]
2081 (3400000.0, 3800000.0]
2082 (8045000.0, 11300000.0]
2083 (5100000.0, 5750000.0]
2084 (2980949.0, 3400000.0]
2085 (3800000.0, 4200000.0]
2086 (4200000.0, 4600000.0]
2087 (5750000.0, 6650000.0]
2088 (2980949.0, 3400000.0]
2089 (5750000.0, 6650000.0]
2090 (4200000.0, 4600000.0]
2091 (11300000.0, 330000000.0]
2092 (3800000.0, 4200000.0]
2093 (6650000.0, 8045000.0]
2094 (6650000.0, 8045000.0]
2095 (12189.999, 2300000.0]
2096 (8045000.0, 11300000.0]
2097 (12189.999, 2300000.0]
2098 (2980949.0, 3400000.0]
2099 (11300000.0, 330000000.0]
2100 (2300000.0, 2980949.0]
2101 (3800000.0, 4200000.0]
2102 (2300000.0, 2980949.0]
2103 (2300000.0, 2980949.0]
2104 (2980949.0, 3400000.0]
2105 (2980949.0, 3400000.0]
2106 (2980949.0, 3400000.0]
2107 (5750000.0, 6650000.0]
2108 (3400000.0, 3800000.0]
2109 (4600000.0, 5100000.0]
2110 (2300000.0, 2980949.0]
2111 (5100000.0, 5750000.0]
2112 (5750000.0, 6650000.0]
2113 (5100000.0, 5750000.0]
2114 (8045000.0, 11300000.0]
2115 (2980949.0, 3400000.0]
2116 (4200000.0, 4600000.0]
2117 (5750000.0, 6650000.0]
2118 (3800000.0, 4200000.0]
2119 (6650000.0, 8045000.0]
2120 (4600000.0, 5100000.0]
2121 (5100000.0, 5750000.0]
2122 (8045000.0, 11300000.0]
2123 (11300000.0, 330000000.0]
2124 (5100000.0, 5750000.0]
2125 (12189.999, 2300000.0]
2126 (6650000.0, 8045000.0]
2128 (12189.999, 2300000.0]
2129 (8045000.0, 11300000.0]
2130 (3400000.0, 3800000.0]
2131 (5750000.0, 6650000.0]
2132 (8045000.0, 11300000.0]
2133 (2980949.0, 3400000.0]
2134 (4600000.0, 5100000.0]
2135 (4600000.0, 5100000.0]
2136 (12189.999, 2300000.0]
2137 (8045000.0, 11300000.0]
2138 (3800000.0, 4200000.0]
2139 (11300000.0, 330000000.0]
2140 (6650000.0, 8045000.0]
2141 (5100000.0, 5750000.0]
2142 (3400000.0, 3800000.0]
2143 (2980949.0, 3400000.0]
2144 (6650000.0, 8045000.0]
2145 (3400000.0, 3800000.0]
2146 (6650000.0, 8045000.0]
2147 (6650000.0, 8045000.0]
2148 (11300000.0, 330000000.0]
2149 (4600000.0, 5100000.0]
2150 (2980949.0, 3400000.0]
2151 (5100000.0, 5750000.0]
2152 (4600000.0, 5100000.0]
2153 (4200000.0, 4600000.0]
2154 (8045000.0, 11300000.0]
2155 (3800000.0, 4200000.0]
2156 (5750000.0, 6650000.0]
2157 (5750000.0, 6650000.0]
2158 (12189.999, 2300000.0]
2159 (12189.999, 2300000.0]
2160 (2300000.0, 2980949.0]
2161 (2980949.0, 3400000.0]
2162 (4600000.0, 5100000.0]
2163 (6650000.0, 8045000.0]
2164 (5100000.0, 5750000.0]
2165 (2980949.0, 3400000.0]
2166 (2300000.0, 2980949.0]
2167 (2980949.0, 3400000.0]
2168 (5100000.0, 5750000.0]
2169 (11300000.0, 330000000.0]
2170 (4200000.0, 4600000.0]
2171 (8045000.0, 11300000.0]
2172 (3800000.0, 4200000.0]
2173 (3400000.0, 3800000.0]
2174 (2980949.0, 3400000.0]
2176 (11300000.0, 330000000.0]
2177 (5100000.0, 5750000.0]
2178 (4600000.0, 5100000.0]
2179 (5100000.0, 5750000.0]
2180 (2980949.0, 3400000.0]
2181 (5750000.0, 6650000.0]
2182 (4600000.0, 5100000.0]
2183 (5750000.0, 6650000.0]
2184 (5750000.0, 6650000.0]
2185 (5750000.0, 6650000.0]
2186 (4200000.0, 4600000.0]
2187 (6650000.0, 8045000.0]
2188 (4200000.0, 4600000.0]
2189 (8045000.0, 11300000.0]
2190 (5750000.0, 6650000.0]
2191 (5750000.0, 6650000.0]
2192 (8045000.0, 11300000.0]
2193 (12189.999, 2300000.0]
2194 (5100000.0, 5750000.0]
2195 (3400000.0, 3800000.0]
2196 (3800000.0, 4200000.0]
2197 (3800000.0, 4200000.0]
2198 (2300000.0, 2980949.0]
2199 (2300000.0, 2980949.0]
2200 (3400000.0, 3800000.0]
2201 (11300000.0, 330000000.0]
2202 (5100000.0, 5750000.0]
2203 (11300000.0, 330000000.0]
2204 (12189.999, 2300000.0]
2205 (5100000.0, 5750000.0]
2206 (5100000.0, 5750000.0]
2207 (8045000.0, 11300000.0]
2208 (2980949.0, 3400000.0]
2209 (12189.999, 2300000.0]
2210 (6650000.0, 8045000.0]
2211 (2300000.0, 2980949.0]
2212 (6650000.0, 8045000.0]
2213 (4200000.0, 4600000.0]
2214 (3800000.0, 4200000.0]
2215 (4200000.0, 4600000.0]
2216 (12189.999, 2300000.0]
2217 (4600000.0, 5100000.0]
2218 (5750000.0, 6650000.0]
2219 (6650000.0, 8045000.0]
2220 (11300000.0, 330000000.0]
2221 (3800000.0, 4200000.0]
2222 (3400000.0, 3800000.0]
2223 (6650000.0, 8045000.0]
2224 (6650000.0, 8045000.0]
2225 (4600000.0, 5100000.0]
2226 (3400000.0, 3800000.0]
2227 (2980949.0, 3400000.0]
2228 (4600000.0, 5100000.0]
2229 (3800000.0, 4200000.0]
2230 (6650000.0, 8045000.0]
2231 (6650000.0, 8045000.0]
2232 (6650000.0, 8045000.0]
2233 (3400000.0, 3800000.0]
2234 (5750000.0, 6650000.0]
2235 (3400000.0, 3800000.0]
2236 (2300000.0, 2980949.0]
2237 (11300000.0, 330000000.0]
2238 (4200000.0, 4600000.0]
2239 (5100000.0, 5750000.0]
2240 (4200000.0, 4600000.0]
2241 (2300000.0, 2980949.0]
2242 (4200000.0, 4600000.0]
2244 (4200000.0, 4600000.0]
2245 (2980949.0, 3400000.0]
2246 (6650000.0, 8045000.0]
2247 (2980949.0, 3400000.0]
2248 (5750000.0, 6650000.0]
2249 (4600000.0, 5100000.0]
2250 (5100000.0, 5750000.0]
2252 (4600000.0, 5100000.0]
2253 (3400000.0, 3800000.0]
2254 (5750000.0, 6650000.0]
2255 (3800000.0, 4200000.0]
2256 (3800000.0, 4200000.0]
2257 (11300000.0, 330000000.0]
2258 (8045000.0, 11300000.0]
2259 (11300000.0, 330000000.0]
2260 (3400000.0, 3800000.0]
2261 (11300000.0, 330000000.0]
2262 (2300000.0, 2980949.0]
2263 (8045000.0, 11300000.0]
2264 (3400000.0, 3800000.0]
2265 (3400000.0, 3800000.0]
2266 (2300000.0, 2980949.0]
2267 (11300000.0, 330000000.0]
2268 (3800000.0, 4200000.0]
2269 (2980949.0, 3400000.0]
2270 (3400000.0, 3800000.0]
2271 (6650000.0, 8045000.0]
2272 (4200000.0, 4600000.0]
2273 (12189.999, 2300000.0]
2274 (12189.999, 2300000.0]
2275 (8045000.0, 11300000.0]
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20109 (11300000.0, 330000000.0]
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20112 (3400000.0, 3800000.0]
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20114 (5100000.0, 5750000.0]
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20116 (5750000.0, 6650000.0]
20117 (4200000.0, 4600000.0]
20118 (12189.999, 2300000.0]
20119 (3800000.0, 4200000.0]
20120 (11300000.0, 330000000.0]
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20124 (3400000.0, 3800000.0]
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20145 (2980949.0, 3400000.0]
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20197 (6650000.0, 8045000.0]
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20251 (5750000.0, 6650000.0]
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23423 (2300000.0, 2980949.0]
23424 (8045000.0, 11300000.0]
23425 (5750000.0, 6650000.0]
23426 (8045000.0, 11300000.0]
23427 (6650000.0, 8045000.0]
23428 (4600000.0, 5100000.0]
23429 (3400000.0, 3800000.0]
23430 (5100000.0, 5750000.0]
23431 (3800000.0, 4200000.0]
23432 (3800000.0, 4200000.0]
23433 (2300000.0, 2980949.0]
23434 (12189.999, 2300000.0]
23435 (12189.999, 2300000.0]
23436 (5100000.0, 5750000.0]
23437 (4200000.0, 4600000.0]
23438 (4200000.0, 4600000.0]
23439 (6650000.0, 8045000.0]
23440 (3400000.0, 3800000.0]
23441 (3400000.0, 3800000.0]
23442 (2300000.0, 2980949.0]
23443 (11300000.0, 330000000.0]
23444 (4600000.0, 5100000.0]
23445 (5750000.0, 6650000.0]
23446 (11300000.0, 330000000.0]
23447 (3400000.0, 3800000.0]
23448 (11300000.0, 330000000.0]
23449 (5750000.0, 6650000.0]
23450 (2980949.0, 3400000.0]
23451 (3800000.0, 4200000.0]
23452 (12189.999, 2300000.0]
23453 (2980949.0, 3400000.0]
23454 (2980949.0, 3400000.0]
23455 (4200000.0, 4600000.0]
23456 (3800000.0, 4200000.0]
23457 (2980949.0, 3400000.0]
23458 (2300000.0, 2980949.0]
23459 (4600000.0, 5100000.0]
23460 (3400000.0, 3800000.0]
23461 (2980949.0, 3400000.0]
23462 (8045000.0, 11300000.0]
23463 (3400000.0, 3800000.0]
23464 (4200000.0, 4600000.0]
23465 (6650000.0, 8045000.0]
23466 (11300000.0, 330000000.0]
23467 (2980949.0, 3400000.0]
23468 (2980949.0, 3400000.0]
23469 (3800000.0, 4200000.0]
23470 (12189.999, 2300000.0]
23471 (6650000.0, 8045000.0]
23472 (3400000.0, 3800000.0]
23473 (5750000.0, 6650000.0]
23474 (5750000.0, 6650000.0]
23475 (3400000.0, 3800000.0]
23476 (8045000.0, 11300000.0]
23477 (12189.999, 2300000.0]
23478 (5750000.0, 6650000.0]
23479 (4200000.0, 4600000.0]
23480 (12189.999, 2300000.0]
23481 (2980949.0, 3400000.0]
23482 (3400000.0, 3800000.0]
23483 (4200000.0, 4600000.0]
23484 (12189.999, 2300000.0]
23485 (3400000.0, 3800000.0]
23486 (12189.999, 2300000.0]
23487 (2980949.0, 3400000.0]
23488 (4200000.0, 4600000.0]
23489 (6650000.0, 8045000.0]
23490 (12189.999, 2300000.0]
23492 (5100000.0, 5750000.0]
23493 (2300000.0, 2980949.0]
23494 (3400000.0, 3800000.0]
23495 (4600000.0, 5100000.0]
23496 (2980949.0, 3400000.0]
23497 (5750000.0, 6650000.0]
23498 (12189.999, 2300000.0]
23499 (5750000.0, 6650000.0]
23500 (2300000.0, 2980949.0]
23501 (5750000.0, 6650000.0]
23502 (6650000.0, 8045000.0]
23503 (3400000.0, 3800000.0]
23504 (4600000.0, 5100000.0]
23505 (2980949.0, 3400000.0]
23506 (5750000.0, 6650000.0]
23507 (6650000.0, 8045000.0]
23508 (5750000.0, 6650000.0]
23509 (2980949.0, 3400000.0]
23510 (2980949.0, 3400000.0]
23511 (4600000.0, 5100000.0]
23512 (11300000.0, 330000000.0]
23513 (8045000.0, 11300000.0]
23514 (11300000.0, 330000000.0]
23515 (4600000.0, 5100000.0]
23516 (11300000.0, 330000000.0]
23517 (2300000.0, 2980949.0]
23518 (5750000.0, 6650000.0]
23519 (2300000.0, 2980949.0]
23520 (3400000.0, 3800000.0]
23521 (8045000.0, 11300000.0]
23522 (2300000.0, 2980949.0]
23523 (4200000.0, 4600000.0]
23524 (3800000.0, 4200000.0]
23525 (5750000.0, 6650000.0]
23526 (5750000.0, 6650000.0]
23527 (3400000.0, 3800000.0]
23528 (4600000.0, 5100000.0]
23529 (4200000.0, 4600000.0]
23530 (2980949.0, 3400000.0]
23531 (5750000.0, 6650000.0]
23532 (8045000.0, 11300000.0]
23533 (6650000.0, 8045000.0]
23534 (3800000.0, 4200000.0]
23535 (8045000.0, 11300000.0]
23536 (5750000.0, 6650000.0]
23537 (3400000.0, 3800000.0]
23538 (8045000.0, 11300000.0]
23539 (3400000.0, 3800000.0]
23540 (12189.999, 2300000.0]
23541 (5100000.0, 5750000.0]
23542 (3400000.0, 3800000.0]
23543 (4600000.0, 5100000.0]
23544 (4600000.0, 5100000.0]
23545 (8045000.0, 11300000.0]
23546 (3400000.0, 3800000.0]
23547 (4200000.0, 4600000.0]
23548 (12189.999, 2300000.0]
23549 (11300000.0, 330000000.0]
23550 (4200000.0, 4600000.0]
23551 (3400000.0, 3800000.0]
23552 (6650000.0, 8045000.0]
23553 (3800000.0, 4200000.0]
23554 (2980949.0, 3400000.0]
23555 (11300000.0, 330000000.0]
23556 (2980949.0, 3400000.0]
23557 (3400000.0, 3800000.0]
23558 (8045000.0, 11300000.0]
23559 (11300000.0, 330000000.0]
23560 (5100000.0, 5750000.0]
23561 (12189.999, 2300000.0]
23562 (2300000.0, 2980949.0]
23563 (5100000.0, 5750000.0]
23564 (2980949.0, 3400000.0]
23565 (5750000.0, 6650000.0]
23566 (11300000.0, 330000000.0]
23567 (2300000.0, 2980949.0]
23568 (8045000.0, 11300000.0]
23569 (12189.999, 2300000.0]
23570 (5100000.0, 5750000.0]
23571 (4600000.0, 5100000.0]
23572 (3800000.0, 4200000.0]
23573 (5750000.0, 6650000.0]
23574 (11300000.0, 330000000.0]
23575 (5100000.0, 5750000.0]
23576 (6650000.0, 8045000.0]
23577 (3400000.0, 3800000.0]
23578 (8045000.0, 11300000.0]
23579 (2300000.0, 2980949.0]
23580 (5100000.0, 5750000.0]
23581 (5750000.0, 6650000.0]
23582 (3400000.0, 3800000.0]
23583 (2980949.0, 3400000.0]
23584 (2300000.0, 2980949.0]
23585 (5750000.0, 6650000.0]
23586 (4200000.0, 4600000.0]
23587 (11300000.0, 330000000.0]
23588 (8045000.0, 11300000.0]
23589 (12189.999, 2300000.0]
23591 (5750000.0, 6650000.0]
23592 (12189.999, 2300000.0]
23593 (2980949.0, 3400000.0]
23594 (2300000.0, 2980949.0]
23595 (4600000.0, 5100000.0]
23596 (3400000.0, 3800000.0]
23597 (12189.999, 2300000.0]
23598 (4200000.0, 4600000.0]
23599 (3400000.0, 3800000.0]
23600 (8045000.0, 11300000.0]
23601 (2300000.0, 2980949.0]
23602 (8045000.0, 11300000.0]
23603 (6650000.0, 8045000.0]
23604 (4200000.0, 4600000.0]
23605 (3400000.0, 3800000.0]
23606 (2300000.0, 2980949.0]
23607 (3400000.0, 3800000.0]
23608 (11300000.0, 330000000.0]
23609 (6650000.0, 8045000.0]
23610 (2300000.0, 2980949.0]
23611 (4200000.0, 4600000.0]
23612 (2980949.0, 3400000.0]
23613 (3800000.0, 4200000.0]
23614 (6650000.0, 8045000.0]
23615 (3400000.0, 3800000.0]
23616 (6650000.0, 8045000.0]
23617 (5100000.0, 5750000.0]
23618 (5100000.0, 5750000.0]
23619 (2980949.0, 3400000.0]
23620 (2300000.0, 2980949.0]
23621 (4600000.0, 5100000.0]
23622 (6650000.0, 8045000.0]
23623 (4200000.0, 4600000.0]
23624 (2300000.0, 2980949.0]
23625 (6650000.0, 8045000.0]
23626 (3800000.0, 4200000.0]
23627 (5750000.0, 6650000.0]
23628 (6650000.0, 8045000.0]
23629 (5100000.0, 5750000.0]
23630 (3400000.0, 3800000.0]
23631 (11300000.0, 330000000.0]
23632 (4600000.0, 5100000.0]
23633 (12189.999, 2300000.0]
23634 (3800000.0, 4200000.0]
23635 (4600000.0, 5100000.0]
23636 (3800000.0, 4200000.0]
23637 (2300000.0, 2980949.0]
23638 (4600000.0, 5100000.0]
23639 (4200000.0, 4600000.0]
23640 (2300000.0, 2980949.0]
23641 (2980949.0, 3400000.0]
23642 (5750000.0, 6650000.0]
23643 (12189.999, 2300000.0]
23644 (4600000.0, 5100000.0]
23645 (12189.999, 2300000.0]
23646 (8045000.0, 11300000.0]
23647 (5100000.0, 5750000.0]
23648 (8045000.0, 11300000.0]
23649 (2980949.0, 3400000.0]
23650 (8045000.0, 11300000.0]
23651 (2980949.0, 3400000.0]
23652 (4200000.0, 4600000.0]
23653 (8045000.0, 11300000.0]
23654 (2300000.0, 2980949.0]
23655 (2300000.0, 2980949.0]
23656 (2300000.0, 2980949.0]
23657 (5750000.0, 6650000.0]
23659 (4600000.0, 5100000.0]
23660 (2300000.0, 2980949.0]
23661 (8045000.0, 11300000.0]
23662 (5750000.0, 6650000.0]
23663 (5750000.0, 6650000.0]
23664 (8045000.0, 11300000.0]
23665 (4200000.0, 4600000.0]
23666 (8045000.0, 11300000.0]
23667 (8045000.0, 11300000.0]
23668 (5750000.0, 6650000.0]
23669 (2980949.0, 3400000.0]
23670 (2300000.0, 2980949.0]
23671 (3400000.0, 3800000.0]
23672 (2980949.0, 3400000.0]
23673 (2980949.0, 3400000.0]
23674 (5750000.0, 6650000.0]
23675 (3400000.0, 3800000.0]
23676 (3400000.0, 3800000.0]
23677 (2300000.0, 2980949.0]
23678 (3400000.0, 3800000.0]
23679 (2300000.0, 2980949.0]
23680 (4600000.0, 5100000.0]
23681 (5100000.0, 5750000.0]
23682 (2300000.0, 2980949.0]
23683 (12189.999, 2300000.0]
23684 (11300000.0, 330000000.0]
23685 (2300000.0, 2980949.0]
23686 (2980949.0, 3400000.0]
23687 (2980949.0, 3400000.0]
23688 (8045000.0, 11300000.0]
23689 (3400000.0, 3800000.0]
23690 (5100000.0, 5750000.0]
23691 (8045000.0, 11300000.0]
23692 (12189.999, 2300000.0]
23693 (4200000.0, 4600000.0]
23694 (8045000.0, 11300000.0]
23695 (2980949.0, 3400000.0]
23696 (2300000.0, 2980949.0]
23697 (11300000.0, 330000000.0]
23698 (12189.999, 2300000.0]

In [79]:
# Подсчет количества квартир с ценой от 38000000
count_last_price = data.query('last_price >= 38000000').shape[0]
print(f'Количество равно - {count_last_price}')

# Построим график, исключив из анализа строки, в которых цена квартиры равна или превышает 38000000 рублей.
data.query('last_price <= 38000000', inplace=True)
# Проверим замену
display(data['last_price'].describe() / 1000000)
data.plot(y='last_price', kind='hist', bins=50, figsize=(15, 5),
          grid=True, title='Цена объекта в ₽')
# Задание названия оси абсцисс
plt.xlabel('Price M.₽')
# Показать график
plt.show()

Количество равно - 100
last_price
count 0.023233
mean 5.747636
std 4.203447
min 0.012190
25% 3.400000
50% 4.600000
75% 6.600000
max 38.000000

No description has been provided for this image

Данные распределены нормально, общая картина ясна. Самая популярная стоимость квартир в районе 3.75 млн.руб

Количество комнат

In [80]:
# Количество комнат
data.plot(y='rooms', kind='hist', bins=30, range=(0, 15), figsize=(10, 5), grid=True, title='Количество комнат')
# Задание названия оси абсцисс
plt.xlabel('Number of rooms')
# Показать график
plt.show()
No description has been provided for this image
In [81]:
# Подсчет количества квартир с количеством комнат от 7, это делается исходя из графика выше

count_rooms = data.query('rooms >= 7').shape[0]
print(f'Количество равно - {count_rooms}')
# Построим график, исключив из анализа строки, где количество комнат составляет 7 или меньше.
data.query('rooms <= 7', inplace=True)
# Проверим замену
display(data['rooms'].describe())

data.plot(y='rooms', kind='hist', bins=50, range=(0, 7), figsize=(10, 5),
          grid=True, title='Количество комнат')
# Задание названия оси абсцисс
plt.xlabel('Number of rooms')

# Показать график
plt.show()

Количество равно - 25
rooms
count 23230.000000
mean 2.027249
std 0.986450
min 0.000000
25% 1.000000
50% 2.000000
75% 3.000000
max 7.000000

No description has been provided for this image

На гистограмме видно, что наибольшее количество объявлений приходится на квартиры с 1 или 2 комнатами. Как и ожидалось, наименее распространены объявления о квартирах с 7 и более комнатами. Квартиры студии тоже не популярны. Распределение данных близко к нормальному.

Высота потолков

In [82]:
# Высота потолков
data.plot(y='ceiling_height', kind='hist', bins=50, figsize=(10, 5), grid=True, title='Высота потолков в м')
# Задание названия оси абсцисс
plt.xlabel('Height')
# Показать график
plt.show()

data.plot(y='ceiling_height', kind='hist', bins=50, range=(0, 10), figsize=(10, 5),
          grid=True, title='Высота потолков в м')
# Задание названия оси абсцисс
plt.xlabel('Height')
# Показать график
plt.show()

data['ceiling_height'].describe()
No description has been provided for this image
No description has been provided for this image
Out[82]:
ceiling_height
count 23230.000000
mean 2.718414
std 0.994902
min 1.000000
25% 2.600000
50% 2.650000
75% 2.700000
max 100.000000

In [83]:
# Подсчет количества квартир с высотой потолков от 5 метров, это делается исходя из графика выше
count_ceiling_high = data.query('ceiling_height >= 5').shape[0]
print(f'Количество равно - {count_ceiling_high}')

# Построим график, исключив из анализа строки, где высота потолков превышает 5 метров или менее 2,5 метров.
data.query('ceiling_height >= 2.5 and ceiling_height <= 5', inplace=True)
# Проверим замену
display(data['ceiling_height'].describe())

data.plot(y='ceiling_height', kind='hist', bins=50, figsize=(10, 5),
          grid=True, title='Высота потолков в м')
# Задание названия оси абсцисс
plt.xlabel('Height')
# Показать график
plt.show()

Количество равно - 33
ceiling_height
count 23125.000000
mean 2.691109
std 0.203862
min 2.500000
25% 2.600000
50% 2.650000
75% 2.700000
max 5.000000

No description has been provided for this image

Больше всего квартир с высотой потолков от ~2,5 до ~3 метров. Медианное значение высоты потолков после коррекции составило 2,65 метра.

Тип этажа квартиры («первый», «последний», «другой»)

In [84]:
# Тип этажа квартиры

data['floor_category'].hist()
Out[84]:
<Axes: >
No description has been provided for this image

Из графика видно, что люди неохотно покупают недвижимость на первых и последних этажах.

Общее количество этажей в доме

In [85]:
# Общее количество этажей в доме
data.plot(y='floors_total', kind='hist', bins=50, figsize=(10, 5), grid=True, title='Общее количество этажей')
# Задание названия оси абсцисс
plt.xlabel('Number of floors')
# Показать график
plt.show()

data['floors_total'].describe()
No description has been provided for this image
Out[85]:
floors_total
count 23125.000000
mean 10.740757
std 6.616084
min 1.000000
25% 5.000000
50% 9.000000
75% 16.000000
max 60.000000

По графику видно, что в датафрейме преобладают здания с этажностью от 6 до 9 этажей. Это, вероятно, связано с тем, что в датафрейме преобладают объявления из Санкт-Петербурга, где в центре города запрещена высотная застройка.

Расстояние до центра города в метрах

In [86]:
# Расстояние до центра города в метрах
data.plot(y='city_centers_nearest', kind='hist', bins=100, figsize=(10, 5), grid=True, title='Расстояние до центра в м')
# Задание названия оси абсцисс
plt.xlabel('Nearest center')
# Показать график
plt.show()

data['city_centers_nearest'].describe()
No description has been provided for this image
Out[86]:
city_centers_nearest
count 17667.000000
mean 14346.586064
std 8590.872948
min 181.000000
25% 9551.000000
50% 13199.000000
75% 16378.500000
max 65968.000000

По графику можно сказать, что в датафрейме чаще всего встречаются данные с отдаленностью от центра в районе 15 км

Расстояние до ближайшего парка

In [87]:
# Расстояние до ближайшего парка
data.plot(y='parks_nearest', kind='hist', bins=100, figsize=(10, 5), grid=True, title='Расстояние до ближайшего парка в м')
# Задание названия оси абсцисс
plt.xlabel('Nearest_parks')
# Показать график
plt.show()

data.plot(y='parks_nearest', kind='hist', bins=100, range=(0, 1000), figsize=(10, 5), grid=True, title='Расстояние до ближайшего парка в м')
# Задание названия оси абсцисс
plt.xlabel('Nearest parks')
# Показать график
plt.show()

data['parks_nearest'].describe()
No description has been provided for this image
No description has been provided for this image
Out[87]:
parks_nearest
count 7747.000000
mean 493.620756
std 343.005180
min 1.000000
25% 289.000000
50% 456.000000
75% 614.000000
max 3190.000000

По данным графика видно, что люди стараются чаще покупать недвижимость ближе к паркам. Самое популярное расстояние варьируется от 400 до 500 метров. Распределение данных близко к нормальному

Изучите, как быстро продавались квартиры (столбец days_exposition)

  • Построим гистограмму
  • Рассчитаем среднее и медиану столбца
  • Опишем, сколько обычно занимает продажа недвижимости
  • Определим какие продажи можно считать быстрыми, а какие — необычно долгими.
In [88]:
data.plot(y='days_exposition', kind='hist', bins=100, figsize=(10, 5), grid=True,
          title='Сколько дней было размещено объявление')
# Задание названия оси абсцисс
plt.xlabel('Number of days')
# Показать график
plt.show()

data.plot(y='days_exposition', kind='hist', bins=100, range=(0, 800), figsize=(10, 5), grid=True,
          title='Сколько дней было размещено объявление')
# Задание названия оси абсцисс
plt.xlabel('Number of days')
# Показать график
plt.show()
# Выведем основные статистические показатели (среднее, стандартное отклонение, минимум, максимум, квартили)
display(data['days_exposition'].describe())

data['days_exposition'].value_counts()
No description has been provided for this image
No description has been provided for this image
days_exposition
count 20066.000000
mean 179.131067
std 217.867201
min 1.000000
25% 45.000000
50% 94.000000
75% 228.750000
max 1580.000000

Out[88]:
count
days_exposition
45.0 872
60.0 538
7.0 231
30.0 205
90.0 184
4.0 176
3.0 157
5.0 147
14.0 142
9.0 139
20.0 138
44.0 138
8.0 134
11.0 133
10.0 127
13.0 126
18.0 124
28.0 124
6.0 123
15.0 123
22.0 123
21.0 122
29.0 120
19.0 120
35.0 117
24.0 116
17.0 116
34.0 113
23.0 111
42.0 111
54.0 110
12.0 107
27.0 107
59.0 106
43.0 106
61.0 105
74.0 104
26.0 101
89.0 100
16.0 99
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Среднее значение столбца составляет ~181 день, а медиана ~94 дней

По графикам видно наличие ярко выраженных выбросов, что также подтверждается анализом квартилей, полученных с помощью метода describe(). Эти выбросы соответствуют значениям 45, 60 и 94 дней

По медианному значению в 94 дней можно сказать, что в среднем квартиры на площадке продаются примерно за 3 месяца.

Быстрой продажей можно считать сделки, завершенные в промежутке от 1 до 14 дней. Долгую продажу можно выделить как продажи, которые занимают 500 дней и более

Определите факторы, которые больше всего влияют на общую (полную) стоимость объекта

  • общая площадь;
  • жилая площадь;
  • площадь кухни;
  • количество комнат;
  • тип этажа, на котором расположена квартира (первый, последний, другой);
  • дата размещения (день недели, месяц, год).

Цена на момент снятия с публикации находится в столбце last_price, построим диаграммы рассеяния, чтобы оценить взаимосвязь столбцов

Общая площадь, жилая площадь, площадь кухни

In [89]:
columns_to_plot = ['last_price', 'total_area', 'living_area', 'kitchen_area']
pd.plotting.scatter_matrix(data[columns_to_plot], figsize=(15, 15))

# Задание названия оси абсцисс
plt.xlabel('last_price vs (total_area, living_area, kitchen_area)')

# Показать график
plt.show()
No description has been provided for this image
In [90]:
data[columns_to_plot].corr()
Out[90]:
last_price total_area living_area kitchen_area
last_price 1.000000 0.778015 0.674727 0.603957
total_area 0.778015 1.000000 0.926520 0.603651
living_area 0.674727 0.926520 1.000000 0.381673
kitchen_area 0.603957 0.603651 0.381673 1.000000

Из приведенных выше графиков можно сделать вывод, что все три показателя, выраженные в квадратных метрах, примерно одинаково влияют на цену продажи. Корреляции в этих столбцах по отношению к цене продаж имеют довольно высокий коэффициент.

Количество комнат

In [91]:
# Построим график
data.plot(x='rooms', y='last_price', kind='scatter', figsize=(15,5),  grid=True, alpha=0.2,
                  title='last_price vs rooms')
# Построим график с ограниченной осью y
plt.ylim(0, 40000000)
# Задание названия оси абсцисс
plt.xlabel('Rooms')
# Показать график
plt.show()
No description has been provided for this image
In [92]:
# Вычислим коэффициент корреляции
data['rooms'].corr(data['last_price'])
Out[92]:
np.float64(0.48867657902104894)

По графику заметно, что зависимость увеличивается до 3 комнат, а дальше идёт на спад.

Тип этажа, на котором расположена квартира (первый, последний, другой)

In [93]:
# Построим график
data.pivot_table(index='floor_category', values='last_price', aggfunc='mean').plot(
    grid=True, figsize=(15, 5), title='Средняя цена по дням недели')
# Задание названия оси абсцисс
plt.xlabel('Floor category')
# Показать график
plt.show()
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По графику видно, что квартиры на первом этаже имеют наименьшую стоимость. Квартиры на последнем этаже лишь немного дешевле, чем на других этажах.

Дата размещения (день недели, месяц, год)

In [94]:
# График средней цены по дням недели

data.pivot_table(index='week_day', values='last_price', aggfunc='mean').plot(
    grid=True, figsize=(15, 5), title='Средняя цена по дням недели')
plt.xlabel('День недели')
plt.ylabel('Средняя цена')
plt.show()

# График средней цены по месяцам
data.pivot_table(index='month', values='last_price', aggfunc='mean').plot(
    grid=True, figsize=(15, 5), title='Средняя цена по месяцам')
plt.xlabel('Месяц')
plt.ylabel('Средняя цена')
plt.show()

# График средней цены по годам
data.pivot_table(index='year', values='last_price', aggfunc='mean').plot(
    grid=True, figsize=(15, 5), title='Средняя цена по годам')
plt.xlabel('Год')
plt.ylabel('Средняя цена')
plt.show()
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По данным графиков видно, что покупатели чаще всего совершают сделки по недвижимости в пятницу (первый график), что приводит к повышению спроса и, следовательно, цены. По второму графику можно заметить, что цены поднимаются к концу года, а в декабре особенно резко возрастают, вероятно, из-за сдачи объектов. Третий график показывает, что с 2014 года цены на недвижимость снижались до 2018 года, после чего начали расти и вернулись на уровень предыдущего года.

Посчитаем среднюю цену одного квадратного метра в 10 населённых пунктах с наибольшим числом объявлений.

  • Выделены населённые пункты с самой высокой и низкой стоимостью квадратного метра.
In [95]:
# Создим своднаую таблицу
locality = data.pivot_table(index='locality_name', values='square_meter_price', aggfunc=['count','mean'])
locality.columns = ['Количество объявлений', 'Средняя цена за квадратный метр']
locality = locality.sort_values('Количество объявлений', ascending=False)
locality = locality[:10]
locality
Out[95]:
Количество объявлений Средняя цена за квадратный метр
locality_name
Санкт-Петербург 15230 111262.971161
поселок Мурино 549 85602.500519
поселок Шушары 439 78551.344545
Всеволожск 397 68719.320400
Пушкин 359 102865.225783
Колпино 336 75352.475583
поселок Парголово 326 90205.423758
Гатчина 306 68757.678644
деревня Кудрово 299 92473.547576
Выборг 235 58238.143141
In [96]:
locality['Средняя цена за квадратный метр'].plot(x='Количество объявлений', y='Средняя цена за квадратный метр', kind='bar', figsize=(15,5),  grid=True,
                  title='Количество объявлений vs Средняя цена за квадратный метр')
plt.xlabel('Количество объявлений')
plt.ylabel('Средняя цена за квадратный метр')
plt.show()
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Определим квартиры в Санкт-Петербурге с помощью столбца locality_name и вычисилим их среднюю стоимость на разном удалении от центра.

  • учтён каждый километр расстояния, известны средние цены квартир в одном километре от центра, в двух и так далее;
  • описано, как стоимость объекта зависит от расстояния до центра города;
  • построен график изменения средней цены для каждого километра от центра Петербурга.
In [97]:
spb_locality = (
    data.pivot_table(index='city_centers_nearest_km', values='last_price', aggfunc=['mean'])
    .round(decimals=2).plot(grid=True, figsize=(15, 5), title='Изменения средней цены от центра Петербурга'))

# Задание названия
plt.xlabel('Расстояние до центра города, км')
plt.ylabel('Цена в руб.')
plt.legend(['Средняя цена'])

# Показать график
plt.show()
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Из графика видно, что в целом, чем дальше недвижимость находится от центра, тем она дешевле. Однако есть несколько выбросов, которые, вероятно, связаны с тем, что на этих расстояниях расположены более населенные пункты, чем деревни и села, что приводит к росту цен.

Общий вывод¶

В данной работе был проведен исследовательский анализ данных с целью изучения факторов, влияющих на продажу квартир в Санкт-Петербурге и соседних населенных пунктах. Анализ охватывал различные аспекты данных, начиная с предобработки и заканчивая выявлением ключевых факторов, определяющих рыночную стоимость недвижимости.

Предобработка Данных

  1. Пропуски в данных:
    Были обнаружены пропуски в нескольких столбцах, включая ceiling_height, living_area и is_apartment и т. д. Пропуски были заполнены логически обоснованными значениями, например, медианными значениями или по аналогии с похожими объектами.

  2. Изменение типов данных:
    Для некоторых столбцов были изменены типы данных для удобства анализа, например, преобразование дат в формат datetime и преобразование категорий в числовые значения для дальнейшего анализа.

  3. Обработка дубликатов:
    В результате анализа были выявлены и устранены дубликаты данных, такие как повторяющиеся объявления о продаже квартир и неявные дубликаты, связанные с разными вариантами написания названий населенных пунктов.

  4. Добавление новых столбцов:
    Для улучшения качества анализа были добавлены новые столбцы, такие как категория удаленности объекта от центра города и цена квадратного метра.

Исследовательский Анализ Данных

  1. Общая площадь недвижимости: Выявлено, что наиболее востребованные квартиры имеют площадь от 30 до 45 м². Наблюдаются выбросы, связанные с элитной недвижимостью, превышающей 120 м².

  2. Площадь жилых помещений: Наиболее популярные квартиры имеют жилую площадь от 16 до 20 м². Наибольший спрос на квартиры с жилой площадью около 20 м².

  3. Площадь кухни: В среднем площадь кухни в квартирах составляет от 6 до 11 кв. м, что соответствует потребностям семей среднего размера.

  4. Цена: Наиболее востребованные квартиры имеют цену от 3 до 5 миллионов рублей, что составляет основной сегмент рынка.

  5. Количество комнат: Двухкомнатные квартиры являются самыми популярными, за ними следуют однокомнатные и трехкомнатные квартиры.

  6. Высота потолков: Большинство квартир имеют стандартную высоту потолков в 2,5-3,0 метра, что соответствует нормам для жилья массового строительства.

  7. Категория этажа: Квартиры, расположенные не на первом и не на последнем этажах, пользуются наибольшим спросом, что связано с предпочтениями покупателей по уровню комфорта.

  8. Этажность здания: Многоквартирные дома средней этажности (от 6 до 9 этажей) наиболее популярны, что связано с особенностями застройки в Санкт-Петербурге и пригородах.

  9. Расстояние до центра города: Квартиры, расположенные ближе к центру, дороже, однако наибольший спрос наблюдается на жилье, находящееся на умеренном удалении от центра, что обусловлено сочетанием цены и доступности.

  10. Расстояние до ближайшего парка: Квартиры, находящиеся рядом с парком, продаются быстрее, что указывает на важность наличия зеленых зон поблизости.

  11. Скорость Продаж: Быстрая продажа недвижимости наблюдается в течение первых 3 месяцев с момента публикации объявления. Продажи, длившиеся более года, указывают на переоцененные объекты или низкий спрос.

Факторы, Влияющие на Цену

  1. Площадь недвижимости:
    Общая, жилая и площадь кухни имеют значительное влияние на цену недвижимости, подтверждая ожидания покупателей относительно комфорта и функциональности жилья.

  2. Количество комнат:
    Двухкомнатные квартиры и трехкомнатные квартиры остаются наиболее популярными среди покупателей, что делает их наилучшим вариантом для инвестиций.

  3. Этаж:
    Квартиры на первом и последнем этажах менее востребованы, что связано с их меньшим удобством и возможными проблемами, такими как шум или недостаток освещения.

  4. Время продажи:
    Пиковые периоды продаж приходятся на весну и осень и конец года в декабре, а остальные зимние месяцы характеризуются снижением активности на рынке недвижимости.

  5. Рейтинг населенных пунктов:
    Санкт-Петербург ожидаемо занял первое место по популярности и стоимости недвижимости, что подтверждает его статус центра экономической и культурной жизни региона.

  6. Зависимость Цены от Расположения: Чем ближе квартира к центру города, тем выше ее стоимость. Однако, стоимость жилья значительно падает с увеличением расстояния до центра, что отражает баланс между доступностью и престижностью.

Заключение В результате исследования были выявлены ключевые факторы, влияющие на стоимость недвижимости в Санкт-Петербурге и пригородах. Эти результаты могут быть использованы для дальнейшего совершенствования моделей ценообразования и разработки рекомендаций для продавцов и покупателей недвижимости.